By Milan Shetti, CEO Rocket Software

If we’ve learned anything over the last few years facing a global pandemic, stalled supply chains, rising inflation, and sinking economies, it’s that change is the new normal in today’s markets.

In response, organizations have invested heavily in digital transformation. IDC forecasts that global spending on digital transformation will reach $2.8 trillion by 2025 — more than double what was spent in 2020.

As organizations amp up their digital transformation initiatives, which are critical for survival in today’s business climate, they must also consider how to modernize and migrate sensitive data and how it is managed and governed. C-suite leaders must have confidence in the data they have on hand to fuel business processes, deliver customer and employee experiences, and improve their operational analytics and insights.

Given the volume of data most organizations have, they need agile technologies that can provide a vast array of services to streamline content management and compliance, leverage automation to simplify data governance, and identify and optimize all of their company’s valuable data.

Ultimately, when evaluating automation technologies, your business needs software that will enable teams to move quickly and easily identify high-priority, sensitive data and to identify and remove redundant, obsolete, and trivial content (ROT) to remain compliant with complex regulatory demands. 

With organizations grappling with how best to streamline data management and compliance, there are four key considerations in doing it effectively.

1. Identification

Businesses need fast and accurate analysis of all their content. Organizations with content-rich processes should look for flexible and scalable automated solutions that can deliver a broad classification of content — reducing the chances of important information slipping through the cracks and allowing teams to quickly identify more types of sensitive data.

2. Action

To support compliance with a governance-first approach to content-rich process automation, businesses must be vigilant when it comes to managing the retention and privacy of documents. This is achievable by automating as much governance decision-making and manual processes as possible. Utilizing automation technology to automatically govern content-rich processes and eliminate mundane, tedious, and repetitive tasks, teams can eradicate many opportunities for human error and free up employees and resources to increase efficiency. 

3. Access

One of the biggest threats to a company’s sensitive data is accessibility. Easily accessible, less secure data is vulnerable to hackers and malware, which, if breached, can have catastrophic consequences for an organization. Teams must look for automation software that can set time and geography parameters around employee accessibility, deny access should a network be breached, and allow redaction across the entire enterprise. 

4. Lifecycle

To successfully manage the entire content lifecycle, businesses must have the ability to place content on legal hold, manage the over-retention of documents, and enable encryption at rest. Rocket Software’s Mobius Content Services platform  delivers this by not only allowing report management teams to encrypt and quickly put content on legal hold, but also providing storage reduction to avoid over-retention and ROT. Mobius can also easily integrate into many shared drives and collaborative platforms to streamline ROT and site auditing.  

With investment growing in digital transformation, organizations must stay competitive — and, for many, data is becoming the critical differentiator. By implementing the right tools now for data automation governance, organizations will be better positioned to maximize it and stay compliant. To learn more about Rocket’s content management solutions, visit the product page.

Data Management

By George Trujillo, Principal Data Strategist, DataStax

Innovation is driven by the ease and agility of working with data. Increasing ROI for the business requires a strategic understanding of — and the ability to clearly identify — where and how organizations win with data. It’s the only way to drive a strategy to execute at a high level, with speed and scale, and spread that success to other parts of the organization. Here, I’ll highlight the where and why of these important “data integration points” that are key determinants of success in an organization’s data and analytics strategy. 

A sea of complexity

For years, data ecosystems have gotten more complex due to discrete (and not necessarily strategic) data-platform decisions aimed at addressing new projects, use cases, or initiatives.  Layering technology on the overall data architecture introduces more complexity. Today, data architecture challenges and integration complexity impact the speed of innovation, data quality, data security, data governance, and just about anything important around generating value from data. For most organizations, if this complexity isn’t addressed, business outcomes will be diluted.

Increasing data volumes and velocity can reduce the speed that teams make additions or changes to the analytical data structures at data integration points — where data is correlated from multiple different sources into high-value business assets. For real-time decision-making use cases, these can be in a memory or database cache. For data warehouses, it can be a wide column analytical table.

Many companies reach a point where the rate of complexity exceeds the ability of data engineers and architects to support the data change management speed required for the business. Business analysts and data scientists put less trust in the data as data, process, and model drift increases across the different technology teams at integration points. The technical debt keeps increasing and everything around working with data gets harder. The cloud doesn’t necessarily solve this complexity — it’s a data problem, not an on-premise versus cloud problem.

Reducing complexity is particularly important as building new customer experiences; gaining 360-degree views of customers; and decisioning for mobile apps, IoT, and augmented reality are all accelerating the movement of real-time data to the center of data management and cloud strategy — and impacting the bottom line. New research has found that 71% of organizations link revenue growth to real-time data (continuous data in motion, like data from clickstreams and intelligent IoT devices or social media).

Waves of change

There are waves of change rippling across data architectures to help harness and leverage data for real results. Over 80% of new data is unstructured, which has helped to bring NoSQL databases to the forefront of database strategy. The increasing popularity of the data mesh concept highlights the fact that lines of business need to be more empowered with data. Data fabrics are picking up momentum to improve analytics across different analytical platforms. All this change requires technology leadership to refocus vision and strategy. The place to start is by looking at real-time data, as this is becoming the central data pipeline for an enterprise data ecosystem.

There’s a new concept that brings unity and synergy to applications, streaming technologies, databases, and cloud capabilities in a cloud-native architecture; we call this the “real-time data cloud.” It’s the foundational architecture and data integration capability for high-value data products. Data and cloud strategy must align. High-value data products can have board-level KPIs and metrics associated with them. The speed of managing change of real-time data structures for analytics will determine industry leaders as these capabilities will define the customer experience. 

Making the right data platform decisions

An important first step in making the right technology decisions for a real-time data cloud is to understand the capabilities and characteristics required of data platforms to execute an organization’s business operating model and road map. Delivering business value should be the foundation of a real-time data cloud platform; the ability to demonstrate to business leaders exactly how a data ecosystem will drive business value is critical. It also must deliver any data, of any type, at scale, in a way that development teams can easily take advantage of to build new applications.   

The article What Stands Between IT and Business Success highlights the importance of moving away from a siloed perspective and focusing on optimizing how data flows through a data ecosystem. Let’s look at this from an analytics perspective.

Data should flow through an ecosystem as freely as possible, from data sources to ingestion platforms to databases and analytic platforms. Data or derivatives of the data can also flow back into the data ecosystem. Data consumers (analytics teams and developers, for example) then generate insights and business value from analytics, machine learning, and AI. A data ecosystem needs to streamline the data flows, reduce complexity, and make it easier for the business and development teams to work with the data in the ecosystem.


IDC Market Research highlights that companies can lose up to 30% in revenue annually due to inefficiencies resulting from incorrect or siloed data. Frustrated business analysts and data scientists deal with these inefficiencies every day. Taking months to on-board new business analysts, difficulty in understanding and trusting data, and delays in business requests for changes to data are hidden costs; they can be difficult to understand, measure, and (more importantly) correct. Research from Crux shows that businesses underestimate their data pipeline costs by as much as 70%.

Data-in-motion is ingested into message queues, publish subscribe messaging (pub/sub), and event streaming platforms. Data integration points occur with data-in-motion in memory/data caches and dashboards that impact real-time decisioning and customer experiences. Data integration points also show up in databases. The quality of integration of data-in-motion and databases impact the quality of data integration in analytic platforms. The complexity at data integration points impacts the quality and speed of innovation for analytics, machine learning, and artificial intelligence across all lines of business.


Standardize to optimize

To reduce the complexity at data integration points and improve the ability to make decisions in real time, the number of technologies that converge at these points must be reduced. This is accomplished by working with a multi-purpose data ingestion platform that can support message queuing, pub/sub, and event streaming. Working with a multi-model database that can support a wide range of use cases reduces data integration from a wide range of single purpose databases. Kubernetes is also becoming the standard for managing cloud-native applications. Working with cloud-native data ingestion platforms and databases enables Kubernetes to align applications, data pipelines, and databases.

As noted in the book Enterprise Architecture as Strategy: Creating a Strategy for Business Execution, “Standardize, to optimize, to create a compound effect across the business.” In other words, streamlining a data ecosystem reduces complexity and increases the speed of innovation with data.

Where organizations win with data

Complexity generated from disparate data technology platforms increases technical debt, making data consumers more dependent on centralized teams and specialized experts.  Innovation with data occurs at data integration points. There’s been too much focus on selecting data platforms based on the technology specifications and mechanics for data ingestion and databases, versus standardizing on technologies that help drive business insights. 

Data platforms and data architectures need to be designed from the onset with a heavy focus on building high-value, analytic data assets and driving revenue, as well as for the ability for these data assets to evolve as business requirements change. Data technologies need to reduce complexity to accelerate business insights. Organizations should focus on data integration points because that’s where they win with data. A successful real-time data cloud platform needs to streamline and standardize data flows and their integrations throughout the data ecosystem.

Learn more about DataStax here.

About George Trujillo:

George is principal data strategist at DataStax. Previously, he built high-performance teams for data-value driven initiatives at organizations including Charles Schwab, Overstock, and VMware. George works with CDOs and data executives on the continual evolution of real-time data strategies for their enterprise data ecosystem. 

Data Management

Carhartt’s signature workwear is near ubiquitous, and its continuing presence on factory floors and at skate parks alike is fueled in part thanks to an ongoing digital transformation that is advancing the 133-year-old Midwest company’s operations to make the most of advanced digital technologies, including the cloud, data analytics, and AI.

The company, which operates four factories in Kentucky and Tennessee and designs all its products at its Dearborn, Mich., headquarters, began its digital transformation roughly four years ago. Today, more than 90% of its applications run in the cloud, with most of its data is housed and analyzed in a homegrown enterprise data warehouse.

Katrina Agusti, a 19-year veteran of the company who was named CIO six months ago, has played a pivotal role retooling the workwear retailer for the modern era, under previous CIO John Hill.

Now Agusti, who began her Carhartt tenure as a senior programmer analyst, is charged with leading the company’s transformation into its next phase, one that is accelerating daily with the barrage of complex technologies changing the global supply chain and business practices, Agusti says.

As part of that transformation, Agusti has plans to integrate a data lake into the company’s data architecture and expects two AI proofs of concept (POCs) to be ready to move into production within the quarter. Like all manufacturers in the information age, Carhartt is also increasing relying on automation and robotics at its service and fulfillment centers as it faces challenges in finding talent on the technology side and in the labor force to meet growing demand.

And demand certainly is on the rise for the workwear manufacturer, which is currently experiencing double-digit growth in all three of its lines of its business — direct to consumer, direct to business, and wholesale.

Tuning a transformation to make the most of data

Carhartt launched its Cloud Express initiative as part of a foundational transformation to shift the company’s 220 applications to Microsoft Azure. Two legacy applications, its warehouse management solution and its payroll and benefits solutions, still run on premises but those applications may soon be replaced in favor of cloud-native solutions, Agusti says.

Moving to the cloud — even amidst the pandemic — was a major win for Carhartt. Aside from the obvious speed to market and scalability gains, the vast improvements in stability, performance, uptime, maintenance, failover monitoring, and alerting has automated many of the costly, time-consuming IT tasks, thereby freeing up the IT team to tackle advanced data analytics and to experiment with other new technologies.

Agusti says Carhartt will likely embrace a multicloud architecture in the long run, but for now she and her team are ramping up their cloud expertise in part through conversations with other CIOs about best practices.

“We’re still learning and building the muscle internally to properly run in the cloud and how to manage in the cloud, and not just the management of systems but how to size them,” she says, adding that she is also homing in on data architecture and retention strategies. “It’s a different beast to manage workloads in the cloud versus workloads on premise. We’re still in that journey.”

Like many CIOs, Carhartt’s top digital leader is aware that data is the key to making advanced technologies work. Carhartt opted to build its own enterprise data warehouse even as it built a data lake with Microsoft and Databricks to ensure that its handful of data scientists have both engines with which to manipulate structured and unstructured data sets.

“Today, we backflush our data lake through our data warehouse. Architecturally, what we’d like to do is bring the data in first into the data lake, whether it is structured or unstructured, and then feed it into our data warehouse,” Agusti says, adding that they continue to design a data architecture that is ideal for different data sets.

She does not currently have plans to retire the homegrown data warehouse in favor of the data lake because the team has customized many types of certified data sets for it.

“The data lake will be more in service to our data science team and consumer-facing teams that are building out journeys using unstructured data to inform those personalization,” Agusti says, noting Carhartt’s six data scientists have built several machine learning models that are currently in test mode.

Two such projects are nearing production, the first of which supports Carhartt’s replication of inventory for its five distribution centers and three different businesses.

“We’re trying to use it for decision support and to plan all of that inventory into different distribution centers based on service levels,” she says, noting that the model can optimize Carhartt’s distribution network by taking into account capacities as well as supply and demand and inventory levels.

The second POC is aimed at helping data scientists collect consumer data that can be leveraged to “personalize the consumer journey,” including demographics information and data from consumer surveys, Agusti says.

The power of tech

Like many CIOs, Agusti’s biggest challenge is change management — especially when it comes to persuading employees that the company’s AI models really work.

“Teams are skeptical that technology can provide the decision support and automation that they do today,” the CIO says. “We have a lot of use cases and we’re running them in POC mode because we need to prove to our end users and business community that these models can make those decisions for you.”

Agusti expects many companies are in this transition mode. “There are different functions along the maturity curve,” she says of the AI efforts under way, “but I think there are so many potential applications that can leverage technology especially in data analytical spaces.”

To pique her resolve about the power of technology, all the CIO has to do is think about how, without investments in technology and talent, the pandemic might have derailed the company’s business.

At first, during the pandemic, many essential workers needed to be equipped with Carhartt work gear for extra protection. As a result, the company’s revenue stream grew in the double digits, even when certain business segments were curtailed due to widespread work stoppages.

Once work stoppages started taking hold, Carhartt gained a rare glimpse into its supply chain, enabling its data analysts to view the steps of the supply chain in exquisite detail, like the individual frames in a film.

“What the pandemic did was create the need for that visibility and proactive exception management,” Agusti says. “Every leg of that journey becomes important when you’re having disruption. It was the catalyst for us to get more granular in the visibility and exception management of every single step in the supply chain.”

Thanks to that visibility — and IT’s push to keep Carhartt’s businesses humming — the company is in a better place with its supply chain. It’s still not at the “predictable” level that it was pre-pandemic, Agusti says, but “we’re starting to see logistical lead times level out and improvements of lead times for goods creation getting better.”

Analytics, Artificial Intelligence, Data Management

Every enterprise needs a data strategy that clearly defines the technologies, processes, people, and rules needed to safely and securely manage its information assets and practices.

As with just about everything in IT, a data strategy must evolve over time to keep pace with evolving technologies, customers, markets, business needs and practices, regulations, and a virtually endless number of other priorities.

Here’s a quick rundown of seven major trends that will likely reshape your organization’s current data strategy in the days and months ahead.

1. Real-time data gets real — as does the complexity of dealing with it

CIOs should prioritize their investment strategy to cope with the growing volume of complex, real-time data that’s pouring into the enterprise, advises Lan Guan, global data and AI lead at business consulting firm Accenture.

Guan believes that having the ability to harness data is non-negotiable in today’s business environment. “Unique insights derived from an organization’s data constitute a competitive advantage that’s inherent to their business and not easily copied by competitors,” she observes. “Failing to meet these needs means getting left behind and missing out on the many opportunities made possible by advances in data analytics.”

The next step in every organization’s data strategy, Guan says, should be investing in and leveraging artificial intelligence and machine learning to unlock more value out of their data. “Initiatives such as automated predictive maintenance on machinery or workforce optimization through operational data are only a few of the many opportunities enabled by the pairing of a successful data strategy with the impactful deployment of artificial intelligence.”

2. In-house data access demands take center stage

CIOs and data leaders are facing a growing demand for internal data access. “Data is no longer just used by analysts and data scientists,” says Dinesh Nirmal, general manager of AI and automation at IBM Data. “Everyone in their organization — from sales to marketing to HR to operations — needs access to data to make better decisions.”

The downside is that providing easy access to timely, relevant data has become increasingly challenging. “Despite massive investments, the data landscape within enterprises is still overly complex, spread across multiple clouds, applications, locations, environments, and vendors,” Nirmal says.

As a result, a growing number of IT leaders are looking for data strategies that will allow them to manage the massive amounts of disparate data located in silos without introducing new risk and compliance challenges. “While the need for data access internally is rising, [CIOs] also have to keep pace with rapidly evolving regulatory and compliance measures, like the EU Artificial Intelligence Act and the newly released White House Blueprint for an AI Bill of Rights,” Nirmal says.

3. External data sharing gets strategic

Data sharing between business partners is becoming far easier and much more cooperative, observes Mike Bechtel, chief futurist at business advisory firm Deloitte Consulting. “With the meaningful adoption of cloud-native data warehouses and adjacent data insights platforms, we’re starting to see interesting use cases where enterprises are able to braid their data with counterparties’ data to create altogether new, salable, digital assets,” he says.

Bechtel envisions an upcoming sea change in external data sharing. “For years, boardroom and server room folks alike have talked abstractly about the value of having all this data, but the geeks among us have known that the ability to monetize that data required it to be more liquid,” he says. “Organizations may have petabytes of interesting data, but if it’s calcified in an aging on-premises warehouse, you’re not going to be able to do much with it.”

4. Data fabric and data mesh adoption rises

Data fabric and data mesh technologies can help organizations squeeze the maximum value out of all the elements in a technical stack and hierarchy in a practical and usable manner. “Many enterprises still utilize legacy solutions, old and new technologies, inherited policies, processes, procedures, or approaches, but wrestle with having to blend it all within a new architecture that enables more agility and speed,” says Paola Saibene, principal consultant at IT advisory firm Resultant.

Mesh enables an organization to draw the information and insights it needs from the environment in its current state without having to radically change it or massively disrupt it. “This way, CIOs can take advantage of [tools] they already have, but add a layer on top that allows them to make use of all those assets in a modern and fast way,” Saibene explains.

Data fabric is an architecture that enables the end-to-end integration of various data pipelines and cloud environments through the use of intelligent and automated systems. The fabric, especially at the active metadata level, is important, Saibene notes. “Interoperability agents will make it look like everything is incredibly well-connected and has been intentionally architected that way,” she says. “As such, you’re able to gain all the insights you need while avoiding having to overhaul your environment.”

5. Data observability becomes business-critical

Data observability extends the concept of data quality by closely monitoring data as it flows in and out of the applications. The approach provides business-critical insights into application information, schema, metrics, and lineage, says Andy Petrella, founder of data observability provider, Kensu, and the author of Fundamentals of Data Observability (O’Reilly, 2022).

A key data observability attribute is that it acts on metadata, providing a safe way to monitor data directly within applications. As sensitive data leaves the data pipeline; it’s collected by a data observability agent, Petrella says. “Thanks to this information, data teams can troubleshoot data issues faster and prevent them from propagating, lowering maintenance costs, restoring trust in data, and scaling up value creation from data,” he adds.

Data observability creates an entirely new solution category, Petrella claims. “CIOs should first understand the different approaches to observing data and how it differs from quality management,” he notes. They should then identify the stakeholders in their data team, since they will be responsible for adopting observability technology.

An inability to improve data quality will likely hinder data team productivity while decreasing data trust across the entire data chain. “In the long term, this could push data activities into the background, impacting the organization’s competitiveness and ultimately its revenue,” Petrella states.

IT leaders are contending with soaring complexity and unfathomable volumes of data spread across the technology stack, observes Gregg Ostrowski, executive CTO of Cisco AppDynamics. “They’re having to integrate a massively expanding set of cloud-native services with existing on-premise technologies,” he notes. “From a data strategy perspective, the biggest trend is the need for IT teams to get clear visualization and insight in their applications irrespective of domain, whether on-premises, in the cloud or hybrid environments.”

6. ‘Data as a product’ begins delivering business value

Data as a product is a concept that aims to solve real-world business problems through the use of blended data captured from many different sources. “This capture-and-analyze approach provides a new level of intelligence for companies that can result in a real, bottom-line impact,” says Irvin Bishop, Jr., CIO at Black & Veatch, a global engineering, procurement, consulting, and construction company.

Understanding how to harvest and apply data can be a game-changer in many ways, Bishop states. He reports that Black & Veatch is working with clients to develop data product roadmaps and establish relevant KPIs. “One example is how we utilize data within the water industry to better manage the physical health of critical infrastructure,” he notes. “Data gives our water clients the ability to predict when a piece of equipment will likely need to be replaced and what type of environmental impact it can withstand based on past performance data.” Bishop says that the approach gives participating clients more control over service reliability and their budgets.

7. Cross-functional data product teams arise

As organizations begin treating data as a product, it’s becoming necessary to establish product teams that are connected across IT, business, and data science sectors, says Traci Gusher, data and analytics leader at business advisory firm EY Americas.

Data collection and management shouldn’t be classified as just another project, Gusher notes. “Data needs to be viewed as a fully functional business area, no different than HR or finance,” she claims. “The move to a data product approach means your data will be treated just like a physical product would be — developed, marketed, quality controlled, enhanced, and with a clear tracked value.”

Analytics, Data Management

Data is the new currency of business. We hear that constantly and it is an accurate description of the value that data provides for the successful operation of a business.  Put simply, organizations with “better” data management and use it more effectively, win in the market.  This blog summarizes a recent podcast that featured Graeme Thompson, CIO of Informatica. 

Mr. Thompson started the podcast with an example that drives home the differences between how data and currency are managed in companies today.  A simple comparison of how a CIO and a CFO would answer four key questions set the tone. 

When the CFO is asked “do you know where all your currency is stored?”, the quick answer is yes.  The CIO can’t say the same for the data.If asked, “can you categorize all of your currency?”, the CFO nods in the affirmative, but the CIO knows that many data labels are vague or inaccurate.Asking the CFO if they know who has access to currency, they are certain they do.  The CIO cannot say the same for corporate data.Finally, when asked if currency can be directed for the most profitable use by the company, the CFO can make a positive assertion.  The CIO knows this is not the case.

These four questions provide the foundation for a strategy to enhance the management of data and creating a data platform that enables an organization to leverage it. 

Taking this strategic view of the data asset and making the data the platform for a successful business is also a fundamental change in the role of the CIO.  Rather than focusing on terabytes, cloud services, or how many laptops are in use, a CIO that is focusing on delivering accurate, documented, consistent, managed, and secured data becomes part the firm’s strategic discussions.  This is a fundamental change that enables the CIO to get “a seat at the table”. 

Informatica is enabling this trend with their Intelligent Data Management Cloud, an end-to-end data management platform that acts as the unifying force for making data the currency of your organization.  For more information on how you can create a data platform that gives the organization the same certainty for its information as it has for its money, visit our website.

Data Architecture

3M Health Information Systems (3M HIS), one of the world’s largest providers of software solutions for the healthcare industry, exemplifies 3M Co.’s legendary culture of innovation. By combining the power of a cloud-based data ecosystem with artificial intelligence (AI) and machine learning (ML), 3M HIS is transforming physician workflows and laborious “back office” processes to help healthcare organizations streamline clinical documentation and billing, enhance security and compliance, and redesign the physician-patient experience.

The cloud served as the foundation for this transformation. Migrating its 3MTM 360 EncompassTM System clients to Amazon Web Services (AWS) is helping 3M HIS improve the capture, management, and analysis of patient information across the continuum of care. 3M 360 Encompass is a collection of applications that work together to help hospitals streamline processes, receive accurate reimbursement, promote compliance, and make data-informed decisions. The cloud-based version of the platform has helped 3M HIS and its clients address three primary challenges: a disjointed patient care journey; the byzantine processes that often inhibit timely and accurate billing, reimbursement, and other record-keeping; and the ongoing need to protect and properly use patient data.

Improving the patient care journey with data and the cloud

The broader objective of 3M HIS’s evolving cloud transformation strategy is to help caregivers improve patient outcomes and staff efficiencies by removing barriers to care and providing access to contextually relevant insights at the time of care, according to Detlef Koll, Vice President of Product Development with 3M HIS. Caregivers now work with consistent, reliable tools within 3M 360 Encompass that improve communication and reduce the types of errors and delays that cause patient anxiety and revenue cycle inefficiencies.

The journey a patient takes through the healthcare system can span years and touch multiple providers, from primary care to specialists, test labs, medical imaging, and pharmacies. During each step, multiple types of data are captured in the patient’s medical record, which serves as an ongoing “narrative” of the patient’s clinical condition and the care delivered. Physician notes from visits and procedures, test results, and prescriptions are captured and added to the patient’s chart and reviewed by medical coding specialists, who work with tens of thousands of codes used by insurance companies to authorize billing and reimbursement.

A complete, compliant, structured, and timely clinical note created in the electronic health record (EHR) empowers many downstream users and is essential for delivering collaborative care and driving appropriate reimbursement. Supporting physicians with cloud-based, speech-enabled documentation workflows, 3M HIS further creates time to care by delivering proactive, patient-specific, and in-workflow clinical insights as the note is being created.

The goal of this automated computer-assisted physician documentation (CAPD) technology is to reduce the cognitive overload on physicians regarding coding requirements while closing gaps in patient care and clinical documentation. Without CAPD closing that loop in real time, errors or ambiguities in the clinical note lead to what Koll describes as an “asynchronous” process, requiring physicians to review and correct the note on a patient seen days earlier, thus taking the physician’s time away from patient care and causing delays in the revenue cycle.

To address the issue, 3M HIS needed a way to semantically integrate information from multiple data sources based on the needs of various use cases, so it deployed AWS data management tools and services, including Amazon RDS, Amazon Redshift, and Amazon Athena, for what Koll calls “opportunistic aggregation of information.” For example, for billing coding, the platform extracts only the relevant billable elements such as an office visit for which a claim will be submitted. This type of flexible, cloud-based data management allows 3M HIS to aggregate different data sets for different purposes, ensuring both data integrity and faster processing. “This is a dynamic view on data that evolves over time,” said Koll.

Improving workflows through intelligent, automated processes

The process for gathering data about a patient’s care, then extracting the billable elements to submit to an insurance company for reimbursement, has long been handled by professional coders who can accurately tag each medical procedure with the correct code out of tens of thousands of possibilities. Errors in that process can lead to rejected claims and additional time required by caregivers to correct any gaps or inconsistencies in clinical documentation, which in turn delays cash flows across the complex network of physicians, hospitals, labs, pharmacies, suppliers, and insurers. 

3M HIS’s cloud transformation strategy addressed this challenge by giving clients access to a new suite of data management and AI/ML tools that deliver levels of processing power, functionality, and scale unthinkable in the former on-premises model.  

“If you had to build some of the capabilities yourself, you would probably never get there,”
said Michael Dolezal, Vice President of 3M  Digital Science Community.  With AWS tools such as Amazon QuickSight and Amazon SageMaker, 3M HIS’s clients can “get there” today: “Now our clients not only have a cloud-based instance for their data, but they gain access to tools they never had before and get the ability to do things they otherwise wouldn’t,” Dolezal said. By bringing 3M 360 Encompass to the AWS Cloud, 3M HIS has been able to scale natural language processing and automation capabilities and leverage tools such as Amazon Textract to improve data input and processing to more efficiently organize a patient’s chart.

Automatic speech recognition to capture the clinical narrative at the point of care, along with AWS AI/ML services, helps 3M HIS aggregate, structure, and contextualize data to enable the development of task-specific workflow applications. For instance, to mitigate the administrative burden on physicians, real-time dictation and transcription workflows can be enhanced with automated, closed-loop CAPD, whereby a physician dictating an admit note can be “nudged” that a certain condition is not fully specified in the note and can fix the gap in real time.

Taking frontline physician-assistive solutions to the next level, embedded virtual assistant technology can automate everyday tasks like placing orders for medications and tests. Innovating incrementally toward smarter and more automated workstreams, the 3M HIS ambient clinical documentation solution makes documentation in the EHR a byproduct of the natural patient-physician conversation and not a separate, onerous task for the doctor. This frees the physician to focus completely on the patient during the visit, thereby transforming the experience of healthcare for all stakeholders.

“We want to reduce the inefficient steps in the old model by unifying and information-enabling workflows so that documentation of the procedure and the coding of that procedure are no longer separate work steps,” said Koll. “It has the potential to save hours of time per day for a doctor.”  

Enhancing the security of patient data

The security and governance of patient data is non-negotiable in healthcare, an industry subject to the most stringent data privacy regulations. Administrators are obligated to make sure patient data is consistently used only for its intended purpose, is processed only by the application it was collected for, and stored and retained according to the specific national regulations involved. The cloud gives 3M HIS more confidence that the data passing through its platform remains secure throughout its journey.

“Using a cloud-based solution means you can apply the same security practices and protocol monitoring across all of your data in a very consistent way,” said Dolezal. The platform ensures a shared responsibility for security across 3M HIS, its clients, and AWS.  

Securing patient data in an on-premises health information system puts the onus to protect that information on the client’s infosec team, with risks compounded by each client’s unique IT infrastructure and internal data access policies. Security by design is one of the underlying operating principles for AWS. With a single set of code to monitor, maintain, and patch, 3M HIS is able to keep its platform current, quickly respond to new threats, and vigorously protect patient data centrally, with more certainty that its clients are protected as well.

4 best practices for data-driven transformation

Dolezal and Koll advise anyone considering moving large sets of data to the cloud to follow some fundamental precepts in designing a new solution:

Start with the client and work backward to a solution:  Be clear on the problem you want to solve and the outcomes you want to deliver to the caregiver and patient and work backward from there to identify the right technology tools and services to help achieve those goals.Don’t over-engineer the solution: Many IT organizations are moving away from traditional point solutions for collecting, storing, and analyzing patient information. To reduce complexity, enhance security, and improve flexibility, consider an end-to-end solution that is easier to deploy and update than traditional on-premises solutions, and lets organizations add new functionality incrementally.Bake in security from the start: In highly regulated industries, such as healthcare and financial services, security regulations demand high levels of security and personal privacy protection. These capabilities must be built in as foundational components of any system used to collect, manage, and analyze that data.Don’t constrain native data: Create a data management strategy that accommodates all types of data and isn’t confined to a specific set of use cases today. With both structured and unstructured data flowing into the system, the future ability to analyze the past means having data schema that doesn’t need to be re-architected.

In an intense environment with a relentless focus on cost reduction and improved clinical outcomes in conjunction with greater patient and physician well-being, 3M HIS helps clients efficiently capture and access patient data, gain meaningful insights from all the data, and drive high-value action to meet complex goals.

Learn more about ways to put your data to work on the most scalable, trusted, and secure cloud.

Cloud Computing, Healthcare Industry

As companies start to adapt data-first strategies, the role of chief data officer is becoming increasingly important, especially as businesses seek to capitalize on data to gain a competitive advantage. A role historically focused on data governance and compliance, the scope of responsibilities for CDOs has since grown, pushing them to become strategic business leaders, according to data from IDC.

According to the survey, 80% of the top KPIs that CDOs report focusing on are business oriented. The top five KPIs for CDOs include operational efficiency, data privacy and protection, productivity and capacity, innovation and revenue, and customer satisfaction and success. And 87% of CXOs said that “becoming a more intelligent enterprise is their top priority by 2025,” with 52% of CDOs reporting to a business leader.

If you’re looking to embark on an executive career as a CDO, you’ll need a strong resume. But you don’t need to feel intimidated when writing your executive-level CV; you just need to do a little research. Here are tips from technology resume experts on how to write the ideal resume for chief data officer positions, along with one shining example.

1. Focus on transformation

Data has become a top priority for businesses large and small, and while some companies have already established a digital strategy, many of them are just getting started. As CDO you’ll likely be tasked with some type of digital or data transformation, whether it’s a complete overall of a company’s data practices, or helping a company improve or advance their data strategy to the next level.

“When preparing a resume for CDO roles, each candidate must consider their audience, as different companies include a range of duties into each role. However, the ability to drive digital technology transformation is going to be the focus,” says Stephen Van Vreede, resume expert at IT Tech Exec.

To demonstrate your ability to lead data transformations, you’ll want to highlight relevant skills such as business strategy, strategic planning, business operations, data governance, goal alignment, data security, data sourcing, technology roadmap development, change management, communication, and team leadership.

Analytics, Careers, Data Management, IT Leadership, Resumes

Decision support systems definition

A decision support system (DSS) is an interactive information system that analyzes large volumes of data for informing business decisions. A DSS supports the management, operations, and planning levels of an organization in making better decisions by assessing the significance of uncertainties and the tradeoffs involved in making one decision over another.

A DSS leverages a combination of raw data, documents, personal knowledge, and/or business models to help users make decisions. The data sources used by a DSS could include relational data sources, cubes, data warehouses, electronic health records (EHRs), revenue projections, sales projections, and more.

The concept of DSS grew out of research conducted at the Carnegie Institute of Technology in the 1950s and 1960s, but really took root in the enterprise in the 1980s in the form of executive information systems (EIS), group decision support systems (GDSS), and organizational decision support systems (ODSS). With organizations increasingly focused on data-driven decision making, decision science (or decision intelligence) is on the rise, and decision scientists may be the key to unlocking the potential of decision science systems. Bringing together applied data science, social science, and managerial science, decision science focuses on selecting between options to reduce the effort required to make higher-quality decisions.

Decision support system examples

Decision support systems are used in a broad array of industries. Example uses include:

GPS route planning. A DSS can be used to plan the fastest and best routes between two points by analyzing the available options. These systems often include the capability to monitor traffic in real-time to route around congestion.Crop planning. Farmers use DSS to help them determine the best time to plant, fertilize, and reap their crops. Bayer Crop Science has applied analytics and decision-support to every element of its business, including the creation of “virtual factories” to perform “what-if” analyses at its corn manufacturing sites.Clinical DSS. These systems help clinicians diagnose their patients. Penn Medicine has created a clinical DSS that helps it get ICU patients off ventilators faster.ERP dashboards. These systems help managers monitor performance indicators. Digital marketing and services firm Clearlink uses a DSS system to help its managers pinpoint which agents need extra help.

Decision support systems vs. business intelligence

DSS and business intelligence (BI) are often conflated. Some experts consider BI a successor to DSS. Decision support systems are generally recognized as one element of business intelligence systems, along with data warehousing and data mining.

Whereas BI is a broad category of applications, services, and technologies for gathering, storing, analyzing, and accessing data for decision-making, DSS applications tend to be more purpose-built for supporting specific decisions. For example, a business DSS might help a company project its revenue over a set period by analyzing past product sales data and current variables. Healthcare providers use clinical decision support systems to make the clinical workflow more efficient: computerized alerts and reminders to care providers, clinical guidelines, condition-specific order sets, and so on.

DSS vs. decision intelligence

Research firm, Gartner, declared decision intelligence a top strategic technology trend for 2022. Decision intelligence seeks to update and reinvent decision support systems with a sophisticated mix of tools including artificial intelligence (AI) and machine learning (ML) to help automate decision-making. According to Gartner, the goal is to design, model, align, execute, monitor, and tune decision models and processes.

Types of decision support system

In the book Decision Support Systems: Concepts and Resources for Managers, Daniel J. Power, professor of management information systems at the University of Northern Iowa, breaks down decision support systems into five categories based on their primary sources of information.

Data-driven DSS. These systems include file drawer and management reporting systems, executive information systems, and geographic information systems (GIS). They emphasize access to and manipulation of large databases of structured data, often a time-series of internal company data and sometimes external data.

Model-driven DSS. These DSS include systems that use accounting and financial models, representational models, and optimization models. They emphasize access to and manipulation of a model. They generally leverage simple statistical and analytical tools, but Power notes that some OLAP systems that allow complex analysis of data may be classified as hybrid DSS systems. Model-driven DSS use data and parameters provided by decision-makers, but Power notes they are usually not data-intensive.

Knowledge-driven DSS. These systems suggest or recommend actions to managers. Sometimes called advisory systems, consultation systems, or suggestion systems, they provide specialized problem-solving expertise based on a particular domain. They are typically used for tasks including classification, configuration, diagnosis, interpretation, planning, and prediction that would otherwise depend on a human expert. These systems are often paired with data mining to sift through databases to produce data content relationships.

Document-driven DSS. These systems integrate storage and processing technologies for document retrieval and analysis. A search engine is an example.

Communication-driven and group DSS. Communication-driven DSS focuses on communication, collaboration, and coordination to help people working on a shared task, while group DSS (GDSS) focuses on supporting groups of decision makers to analyze problem situations and perform group decision-making tasks.

Components of a decision support system

According to Management Study HQ, decision support systems consist of three key components: the database, software system, and user interface.

DSS database. The database draws on a variety of sources, including data internal to the organization, data generated by applications, and external data purchased from third parties or mined from the Internet. The size of the DSS database will vary based on need, from a small, standalone system to a large data warehouse.DSS software system. The software system is built on a model (including decision context and user criteria). The number and types of models depend on the purpose of the DSS. Commonly used models include:
Statistical models. These models are used to establish relationships between events and factors related to that event. For example, they could be used to analyze sales in relation to location or weather.
Sensitivity analysis models. These models are used for “what-if” analysis.
Optimization analysis models. These models are used to find the optimum value for a target variable in relation to other variables.
Forecasting models. These include regression models, time series analysis, and other models used to analyze business conditions and make plans.
Backward analysis sensitivity models. Sometimes called goal-seeking analysis, these models set a target value for a particular variable and then determine the values other variables need to hit to meet that target value.
DSS user interface. Dashboards and other user interfaces that allow users to interact with and view results.

Decision support system software

According to Capterra, the popular decision support system software includes:

Checkbox. This no-code service automation software for enterprises uses a drag-and-drop interface for building applications with customizable rules, decision-tree logic, calculations, and weighted scores.Yonyx. Yonyx is a platform for creating DSS applications. It features support for creating and visualizing decision tree–driven customer interaction flows. It especially focuses on decision trees for call centers, customer self-service, CRM integration, and enterprise data.Parmenides Edios. Geared for midsize/large companies, Parmenides Eidos provides visual reasoning and knowledge representation to support scenario-based strategizing, problem solving, and decision-making.XLSTAT. XLSTAT is an Excel data analysis add-on geared for corporate users and researchers. It boasts more than 250 statistical features, including data visualization, statistical modeling, data mining, stat tests, forecasting methods, machine learning, conjoint analysis, and more.1000minds is an online suite of tools and processes for decision-making, prioritization, and conjoint analysis. It is derived from research at the University of Otago in the 1990s into methods for prioritizing patients for surgery.Information Builders WebFOCUS. This data and analytics platform is geared for enterprise and midmarket companies that need to integrate and embed data across applications. It offers cloud, multicloud, on-prem, and hybrid options.QlikView is Qlik’s classic analytics solution, built on the company’s Associative Engine. It’s designed to help users with their day-to-day tasks using a configurable dashboard.SAP BusinessObjects. BusinessObjects consists of reporting and analysis applications to help users understand trends and root causes.TIBCO Spotfire. This data visualization and analytics software helps users create dashboards and power predictive applications and real-time analytics applications.Briq is a predictive analytics and automation platform built specifically for general contractors and subcontractors in construction. It leverages data from accounting, project management, CRM, and other systems, to power AI for predictive and prescriptive analytics.Analytics, Data Science

By Bryan Kirschner, Vice President, Strategy at DataStax

One of the most painful – and pained – statements I’ve heard in the last two years was from an IT leader who said, “my team is struggling to find ways that our company’s data could be valuable to the business.”

Contrast this with what a financial services CIO told me: “Our CEO told every line of business general manager you now have a second job: you’re the general manager of the data produced in your line of business.”

The latter case is as it should be. In a pre-digital world, there would be no doubt that the people running a business function – sales, service, support, or production – should be using all the information available to them to drive better results.

But many organizations took a detour, misled by a fundamentally flawed assumption that because some data is digital in nature and technical skills are necessary to ensure it is properly stored, secured, and made available, those same technologists should be on the hook for finding new ways for business managers to leverage the data.

Wanted: Real-time data skills

Leading organizations have proven there’s a better way forward – but success can’t be taken for granted. Among all respondents surveyed for the latest State of the Data Race report, complexity, cost, and accessibility are cited as the top three challenges they face in leveraging real-time data. In contrast,  the number one challenge among those most accomplished at driving value with real time data today is the availability of the necessary skills in their business units to leverage it.

It’s likely a hangover from the old way of doing things. The good news is that there’s a cure — in the form of a clear playbook for making progress toward equipping business managers

Your technology teams should indeed be accountable for understanding the capabilities of best of breed tools – and making them available widely in your organization.

But everyone — not just technologists, but also business leaders — must have both accountability and skills for using real-time data to drive the business and grow revenue.

Consider pharma giant Novartis (as detailed in this Harvard Business Review article). Over the past decade, the company invested heavily in data platforms and data integration. But it found that these investments only resulted in spotty success. Data scientists had little visibility into the business units, and, conversely, leaders from  sales, supply chain, HR, finance, and marketing weren’t embracing the available data. Once data scientists were paired with business employees with insight into where efficiency and performance improvements were needed, and once frontline organization employees were trained to use data for innovation, the intensity and impact of transformation accelerated.

New ways of working

A clearer sense of a shared mission, along with a stronger common understanding of capabilities of modern technology and greater shared intimacy with business processes and customer experiences further pays off by opening the door to new ways of working.

Take banking, one industry where developers are critical to success in delivering new services for customers, and where incumbents must contend with a growing fintech ecosystem of aspiring disruptors. Goldman Sachs is embedding software developers deeper into the business where–in the words of the CIO– “we want them to answer the ‘why’ questions that get to the business purpose behind their work.”

In the State of the Data Race report, 91%  of respondents from organizations with a strategic focus deploying apps that use data in real-time said that developers, business owners, and data scientists are working in cross-functional teams. Compare that to organizations who are still early on in their real-time data journey: only 67% of them claim to have this cross-functional coordination.

The other side of the coin is AI and ML, which are integrally related to activating data in real time. Some 93% of those with AI and ML in wide production are organized into cross functional teams versus 63% among those in the early days of AI/ML deployment.

Leveraging real-time data used to be a technology problem. Complex, legacy data architectures can still cause challenges, but the data technology landscape — assisted significantly by advances in the open source community — has advanced more than far enough to make real-time capabilities available to organizations of all sizes. The primary challenge real-time data leaders face is a clear indicator of this. Now, companies like Goldman Sachs and Novartis are working to ensure that the real-time data they’ve made readily available turns into real-time results. 

Learn more about DataStax here.

About Bryan Kirschner:

Bryan is Vice President, Strategy at DataStax. For more than 20 years he has helped large organizations build and execute strategy when they are seeking new ways forward and a future materially different from their past. He specializes in removing fear, uncertainty, and doubt from strategic decision-making through empirical data and market sensing.

Data Management, IT Leadership

For probably the umpteenth time, we use the term “garbage in, garbage out” when summarizing problems with data quality. It has indeed become a cliché. Various industry studies have uncovered the high cost of bad data, and it’s estimated that poor data quality costs organizations an average of $12 million yearly. Data teams waste 40% of their time troubleshooting data downtime, even at mature data organizations, and utilizing advanced data stacks.

Data quality, which has always been a critical component of enterprise data governance, remains an Achilles heel for CIOs, CCOs, and CROs. In fact, data quality has become even more challenging to tackle with the prolific increase in data volume and types — structured, unstructured, and semi-structured data.

Data quality is not just a technology problem and never will be because we rarely think of the quality of the data we source when implementing new business initiatives and technology. Technology is only an enabler, and to get the most from the technology, we need to think about the business processes and look for opportunities to re-engineer or revamp these business processes when we start a new technology project. Some of the aspects of understanding these business processes are:

What data do we need?Do we understand the sources of this data?Do we have control over these sources?Do we need to apply any transformations (i.e., changes to this data)?Most importantly, do our end users trust the data for their usage and reporting?

These questions sound basic and obvious. However, most organizations have trust issues with their data. The end users rarely know the source of truth, so they end up building their data fiefdoms, creating their own reports, and maintaining their own dashboards.

Eventually, this causes ‘multiple sources of ‘truth,’ each being a different version of the other. As a result, this causes sleepless nights, especially when we want to submit a regulatory report, make any executive decisions, or submit SEC filings. Not only is this wasting valuable engineering time, but it’s also costing precious revenue and diverting attention away from initiatives moving the business’s needle. In addition, this is a misuse of data scientists’ core skills and adds additional costs and time that could be better used for the organization’s business priorities.

Over time, data quality issues have become more extensive, complex, and costlier to manage. A survey conducted by Monte Carlo suggests that nearly half of all organizations measure data quality most often by the number of customer complaints their company receives, highlighting the ad hoc nature of this vital element of modern data strategy. Most organizations decide to address this issue in a piecemeal fashion that is a practical approach but requires a tremendous effort to understand the data, document the lineage, identify data owners, identify key data elements (KDE), maintain these KDEs, and apply the data governance lifecycle to the data.

No wonder this is only a tactical solution; sooner or later, we need to start working on another tactical project to resolve the issues caused by the previous tactical project and so on. This means an endless cycle of massive spending on IT, frustration because of low return on investment from technology projects, and buying new technology products that promise a total overhaul.

What is data quality management?

Data quality management (DQM) is the set of procedures, policies, and processes an enterprise uses to maintain reliable data in a data warehouse as a system of record, golden record, master record, or single version of the truth. First, the data must be cleansed using a structured workflow involving profiling, matching, merging, correcting, and augmenting source data records. DQM workflows must also ensure the data’s format, content, handling, and management comply with all relevant standards and regulations.

So how do we tackle data quality with a proactive approach? There are a few options, from the traditional approach to the real-time solution.

Traditional approach: Data quality at the sourceThis is the traditional and, in most cases, the best approach to handling data qualityThis includes identifying all the data sources (external and internal)Documenting the data quality requirements and rulesApplying these rules at the source level (in the case of external sources, we apply these rules where the data enters our environment)Once the quality is handled at the source level, we publish this data for the end users through applications such as a data lake or a data warehouse. This data lake or warehouse becomes the “system of insight” for everyone in the organization.Pros of this approach:Most reliable approachOne-time and strategic solutionIt helps you with optimizing your business processesCons of this approachWe need a cultural shift to look at data quality at the source level, ensuring this is applied every time there is a new data source.This is possible only with executive sponsorship, i.e., a top-down decision-making approach, making it an integral part of every employee’s daily activity.Data owners must be ready to invest time and funding to implement data quality at the sources they are responsible for.Implementation of a data quality management toolModern DQM tools automate profiling, monitoring, parsing, standardizing, matching, merging, correcting, cleansing, and enhancing data for delivery into enterprise data warehouses and other downstream repositories. The tools enable creating and revising data quality rules. They support workflow-based monitoring and corrective actions, both automated and manual, in response to quality issues.This approach includes working with the business stakeholders to develop an overall data quality strategy and framework and selecting and implementing the best tool for that framework.The implemented tool should be able to discover all data, profile it, and find patterns. The tool then needs to be trained with data quality rules.Once the tool is trained to a satisfactory level, it starts applying the rules, which helps improve the overall data quality.The training of the tool is perpetual — it keeps learning more as you discover and input the new rules.Pros of this approach:Easy to implement and quick resultsThere is no need to separately work on in-depth lineage documentation (tool automates the data lineage) and governance methodology; we need to define the DQ workflows so tools can automate those.Cons of this approach:Training of the tool requires a good understanding of data and data quality requirementsThere is a tendency to expect that everything will be automated. This is not the case.This is not a strategic solution; it does not help with business process improvement.

Based on the above considerations, we believe the best approach is a combination of the traditional and the DQM tools approach:

First, set up a business-driven data quality framework and an organization responsible for supporting it.Second, define an enterprise DQ philosophy: “Whoever creates the data owns the data.” Surround this with guiding principles and appropriate incentives. Organize around domain-driven design and treat data as a product.Third, develop an architectural blueprint that treats good data and bad data separately and deploy a robust real-time exception framework that notifies the data owner of data quality issues. This framework should include a real-time dashboard highlighting success and failure with clear and well-defined metrics. Bad data should never flow into the good data pipeline.Fourth, incorporating this holistic DQ ecosystem should be mandated for each domain/source/application in a reasonable timeframe and every new application going forward.

Data quality remains one of the foremost challenges for most organizations. There is no guaranteed approach to solving this problem. One needs to look at the various factors, such as the organization’s technology landscape, legacy architecture, existing data governance operating model, business processes, and, most importantly, the organizational culture. The problem cannot be solved only with new technology or by adding more people. It needs to be a combination of business process re-engineering, a data-driven decision-making culture, and the ability to use the DQ tools most optimally. It is not a one-time effort, but a lifestyle change for the organization.

Learn more about Protiviti data and analytics services.

Data Management