Companies today face disruptions and business risks the likes of which haven’t been seen in decades. The enterprises that ultimately succeed are the ones that have built up resilience.

To be truly resilient, an organization must be able to continuously gather data from diverse sources, correlate it, draw accurate conclusions, and in near-real time trigger appropriate actions. This requires continuous monitoring of events both within and outside an enterprise to detect, diagnose, and resolve issues before they can cause any damage.  

This is especially true when it comes to enterprise procurement. Upwards of 70% of an organization’s revenue can flow through procurement. This highlights the critical need to detect potential business disruptions, spend leakages (purchases made at sub-optimal prices by deviating from established contracts, catalogs, or procurement policies), non-compliance, and fraud. Large organizations can have a dizzying array of data related to thousands of suppliers and accompanying contracts.

Yet amassing and extracting value from these large amounts of data is difficult for humans to keep up with, as the number of data sources and volume of data only continues to grow exponentially. Current data monitoring and analysis methods are no longer sufficient.

“While periodic spend analysis was okay up until a few years ago, today it’s essential that you do this kind of data analysis continuously, on a daily basis, to spot issues and address them quicker,” says Shouvik Banerjee, product owner for ignio Cognitive Procurement at Digitate.

Enterprises need a tool that continuously monitors data so they can use their funds more effectively. Companies across industries have found success with ignio Cognitive Procurement, an AI-based analytics solution for procure-to-pay. The solution screens purchase transactions to detect and predict anomalies that increase risk, spend leakage, cycle time, and non-compliance.

For example, the product flags purchase requests with suppliers who have a poor track record of compliance with local labor laws. Likewise, it flags urgent purchases whose fulfillment is likely to be delayed based on patterns observed in similar transactions in the past.  It also flags invoices that need to be prioritized to take advantage of early payment discounts.

“It’s a system of intelligence versus other products in the market, which are systems of record,” says Banerjee. Not only does ignio Cognitive Procurement analyze an organization’s array of transactions, it also takes into account relevant market data on suppliers and categories on a daily basis.

ignio Cognitive Procurement is unique for its ability to correlate what’s currently happening in the market with what’s going on inside an organization, and it makes specific recommendations to stakeholders. For example, the solution can simplify category managers’ work, helping them source the best deals for their company, or make decisions such as whether to place an order now or hold off for a month.

Charged with finding the best suppliers and monitoring their success within the context of the market, category managers work better and smarter when they can tap into ignio Cognitive Procurement.

ignio Cognitive Procurement also identifies other opportunities to save money and improve the effectiveness of procurement. For instance, the solution proactively makes business recommendations that seamlessly take into account not only price, but also a variety of key factors like timeliness, popularity, external market indicators, suppliers’ market reputation, and their legal, compliance, and sustainability records.

“Companies also use the software to analyze that part of spend that’s not happening through contracts,” says Banerjee, “and they’ve been able to identify items which have significant price variance.”

To avoid irreversible damage or missed opportunities and to keep a competitive advantage, organizations across industries urgently need an AI-based analytics solution for procure-to-pay that can augment their human capabilities.

To learn more about Digitate’signio Cognitive Procurement, click here.

Analytics, IT Leadership

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

Freudenberg Home and Cleaning Solutions (FHCS), the winner of the 50th Anniversary Legend award of this year’s SAP Innovation Awards 2022, has been providing market-leading cleaning solutions that keep millions of homes worldwide hygienic and safe since 1849. 

The Challenge: Planning in Silos 

At the start of the project, Freudenberg Home and Cleaning Solutions, which operates in 35 countries, had a disjointed supply chain planning process. Information was collected from multiple, disparate data sources, and planners were using different tools. This hampered the company from having an enterprise-wide view. The company also wanted to improve forecasting accuracy by harnessing the power of intelligent technologies.

Achieve 10x faster-planning cycles despite having larger data volumes 

FHCS integrated its landscape built on SAP ERP and SAP Business Warehouse with specialized forecasting in SAP Integrated Business Planning (IBP). This enabled the company to generate simulations, planning, and reporting solutions based on SAP Analytics Cloud. Connecting the sales, and financial data with production volume data and establishing a single centralized data warehouse enabled planners to understand the profit and loss impact of different planning scenarios. 

“Shifting descriptive analytics to predictive analytics is a huge undertaking for most companies in their digital transformation. With enterprise-wide planning, we built a simulation platform to establish confidence in our predictions and ensure a smooth transition to predictive steering,” said Jochen Moelber, CIO of FHCS. 

Switching from a highly decentralized forecasting process to harmonized planning and forecasting helped Freudenberg Home and Cleaning Solutions significantly improve decision-making. In addition, it helped the leading company to generate more granular planning down to an individual product and customer. 

By standardizing forecasting processes across its consumer products division, the household products manufacturer increased planning accuracy and enabled an enterprise-wide view that resulted in 10X faster-planning cycles despite larger data volumes and greater granularity. 

Act fast when disruption happens 

Siloed processes and disconnected systems are the nightmares of businesses. They not only make it difficult to get an overall picture across the entire company but also make businesses vulnerable to possible risks. Supply chains have been experiencing various challenges caused by the COVID-19 pandemic, and this means that supply chain planners need to get harmonized, detailed, and enterprise-wide forecasting information to run the business effectively. 

“In a rapidly fluctuating market and with continuing supply chain challenges caused by the COVID- 19 pandemic, forecasting is crucial for us. We need it to ensure we produce enough of the right products at the right time and understand the financial impact of various planning scenarios on our P&L. By harmonizing our planning processes across multiple regions and business functions, we can align operational planning with financial performance,” said Franco Giacomini, Vice President Consumer Europe, Freudenberg Home and Cleaning Solutions GmbH. 

It now takes Freudenberg Home and Cleaning Solutions only 2 days to create an initial top-down production plan at the beginning of the planning phase. Combining both operational and financial data as well as detailed product volumes and raw materials helped their business achieve greater transparency. With advanced simulations, Freudenberg Home and Cleaning Solutions can now generate immediate insights into the impact of variables on product groups and their operational impact on different planning scenarios.  

Save significant time with reporting automation 

Carrying out planning processes manually by using spreadsheets is not only a time-consuming activity but also error-prone, which adds more anxiety to the ongoing supply chain processes. Knowing this, Freudenberg Home and Cleaning Solutions aimed to standardize and automate reporting across different regions. 

“By helping harmonize our planning processes across multiple regions and business functions, SAP Analytics Cloud enables us to align operational planning with financial performance,” FrancoGiacomini explained. 

With a simplified planning process and straightforward user experience enabled by SAP Analytics Cloud, non-technical business users gained the ability to explore data and run complex simulations. They also spend less time on repetitive report preparation, which frees up the team for higher-value work – such as analyses of future trends.  

Would you like to learn more about how Integrated Business Planning solutions can help supply chains be more resilient? Check out the recent IDC Analyst Connection “Build a More Resilient Supply Chain”.

Data Management

Ease of implementation and return on investment (ROI), combined with ease of use, continue to dominate the business to business (B2B) software buying process, according to a report from software marketplace G2.

The report, which is based on a survey of 1,002 global decision-makers with responsibility for, or influence over, purchase decisions for departments, multiple departments, operating units, or entire businesses, showed that at least 93% of respondents indicate the quality of the implementation process is very important when deciding whether to renew a software product.

The respondents said they are looking for the least amount of friction while adding a new solution or software to their technology stack and that ease of implementation can add to the frictionless experience, according to the report.

In fact, 77% of respondents indicated they have either worked with a vendor’s implementation team or have worked with a third-party vendor for implementation, as opposed to 34% of respondents indicating that they handle implementation with their internal teams.

These implementation teams play a pivotal role as they shape an opinion about vendors and can help make contract renewals easier, the report noted.

Pricing no longer an effective sales tool

Pricing, according to the respondents, was the second-least favored factor in the buying process. The survey showed that sticker price is no longer a sales tool and has been replaced by proof of return in investment.

The decision makers ranked ease of implementation as the top important factor while ROI within six months and ease of use were the second and third most important factors among 12 other considerations, including price, as part of the buying process.

The survey data showed that these decision makers want to achieve ROI quickly and believe that an easy implementation process combined with an easy-to-use product may help them generate returns faster.

Lower switching costs affect renewal rates

At least 53% of respondents surveyed said they conduct research and consider alternatives when a product is up for renewal as opposed to 45% claiming they renew the software they already use without considering other options. This phenomenon can be attributed to increasing options and lower switching costs, the report showed.

However, the group of decision makers that renews a product without considering options is growing slowly. There has been a 3% increase year-on-year for the same category, according to the report.

Vendors must make information such as proof of ROI available in early stages of adoption in places where these decision makers frequent for researching new products, as a majority of decision makers are still looking for alternative products, the report said.

At least 76% of respondents said product and service review websites are trustworthy and transparency in the validation of the reviews is key. More than 33% of decision-makers surveyed said transparent validation of reviews is the most helpful feature when using online software or service review sites.

The report also shows that most enterprises have a six-month contract period, which leaves limited time for vendors to make an impression on the buyer. At least 57% of respondents said they have a six-month period compared to just 11% stating that they have two-year or multiyear contracts in place.

Buying directly from vendors is slowing down

Buyers are slowly shying away from buying directly from vendors, the report highlighted. Only 60% of respondents said they bought directly from vendors, a 9% decrease from the previous year.

Alternatively, buyers are increasingly purchasing software from third-party marketplaces and value-added resellers (VARs), according to the report.

At least 28% of respondents said they were buying software from third-party marketplaces, an increase of 6% from the previous year’s survey. Further, 11% of respondents said they were buying software from VARs, a 4% increase year-on-year.

Buying committee changes complicate purchase process

The B2B buying journey, according to the report, is becoming increasingly complex due to changes in buying committees. At least 80% of respondents said their enterprises or organizations have such committees in place.

More than 67% of respondents said buying-decision makers are changed frequently or nearly always during the software buying process, up by 15% from previous year’s survey.

Another challenge is that the buying committee is more likely to have changed at the time of renewal from when the product was originally purchased, the report showed.

IT Strategy, Software Licensing

Most organizations understand the profound impact that data is having on modern business. In Foundry’s 2022 Data & Analytics Study, 88% of IT decision-makers agree that data collection and analysis have the potential to fundamentally change their business models over the next three years.

The ability to pivot quickly to address rapidly changing customer or market demands is driving the need for real-time data. But poor data quality, siloed data, entrenched processes, and cultural resistance often present roadblocks to using data to speed up decision making and innovation.

We asked the CIO Experts Network, a community of IT professionals, industry analysts, and other influencers, why real-time data is so important for today’s business and how data helps organizations make better, faster decisions. Based on their responses, here are four recommendations for improving your ability to make data-driven decisions. 

Use real-time data for business agility, efficient operations, and more

Business and IT leaders must keep pace with customer demands while dealing with ever-shifting market forces. Gathering and processing data quickly enables organizations to assess options and take action faster, leading to a variety of benefits, said Elitsa Krumova (@Eli_Krumova), a digital consultant, thought leader and technology influencer.

“The enormous potential of real-time data not only gives businesses agility, increased productivity, optimized decision-making, and valuable insights, but also provides beneficial forecasts, customer insights, potential risks, and opportunities,” said Krumova.

Other experts agree that access to real-time data provides a variety of benefits, including competitive advantage, improved customer experiences, more efficient operations, and confidence amid uncertain market forces:

“Business operations must be able to make adjustments and corrections in near real time to stay ahead of the competition. Few companies have the luxury of waiting days or weeks to analyze data before reacting. Customers have too many options. And in some industries — like healthcare, financial services, manufacturing, etc., — not having real-time data to make rapid critical adjustments can lead to catastrophic outcomes.” — Jack Gold (@jckgld), President and Principal Analyst at J. Gold Associates LLC.

“When insights from the marketplace are not transmitted in real time, the ability to make critical business decisions disappears. We’ve all experienced the pain of what continues to happen with the disconnect between customer usage metrics and gaps in supply chain data.” — Frank Cutitta (@fcutitta), CEO and Founder, HealthTech Decisions Lab

“Operationally, think of logistics. Real-time data provides the most current intelligence to manage the fleet and delivery, for example. Strategically, with meaningful real-time data, systemic issues are easier to identify, portfolio decisions faster to make, and performance easier to evaluate. At the end of the day, it drives better results in safety, customer satisfaction, the bottom line, and ESG [environmental, social, and governance].” — Helen Yu (@YuHelenYu), Founder and CEO, Tigon Advisory Corp.

“Businesses are facing a rapidly evolving set of threats from supply chain constraints, rising fuel costs, and shipping delays. Taking too much time to make a decision based on stale data can increase overall costs due to changes in fuel prices, availability of inventory, and logistics impacting the shipping and delivery of products. Organizations utilizing real-time data are the best positioned to deal with volatile markets.” — Jason James (@itlinchpin), CIO at Net Health

Build a foundation for continuous improvement

The experts offered several practical examples of how real-time data can help deliver continuous improvement in a variety of areas across the business, with the help of automation, which is a key capability for making data actionable.

“In the process of digital transformation, businesses are moving from human-dependent to digital business processes,” said Nikolay Ganyushkin (nikolaygan), CEO and Co-founder of Acure. “This means that all changes, all transitions, are instantaneous. The control of key parameters and business indicators should also be based on real-time data, otherwise such control will not keep up with the processes.”

Real-time data and automated processes present a powerful combination for improving cybersecurity and resiliency.

“When I was coming up in InfoSec, we could only do vulnerability scanning between midnight and 6 am. We never got good results because systems were either off, or there was just nothing going on at those hours,” said George Gerchow (@georgegerchow), CSO and SVP of IT, Sumo Logic. “Today, we do them at the height of business traffic and can clearly see trends of potential service outages or security incidents.”

Will Kelly (@willkelly), an analyst and writer focused on the cloud and DevOps, said that harnessing real-time data is critical “in a world where delaying business and security decisions can prove even more costly than just a couple of years ago. Tapping into real-time data provides decision-makers with immediate access to actionable intelligence, whether a security alert on an attack in-progress or data on a supply chain issue as it happens.”

Real-time data facilitates timely, relevant, and insightful decisions down to the business unit level, said Gene De Libero (@GeneDeLibero), Chief Strategy Officer at GeekHive.com. Those decisions can have a direct impact on customers. “Companies can uncover and respond to changes in consumer behavior to promote faster and more efficient personalization and customization of customer experiences,” he said.

Deploy an end-to-end approach to storing, accessing, and analyzing data

To access data in real time — and ensure that it provides actionable insights for all stakeholders — organizations should invest in the foundational components that enable more efficient, scalable, and secure data collection, processing, and analysis. These components, including cloud-based databases, data lakes, and data warehouses, artificial intelligence and machine learning (AI/ML) tools, analytics, and internet of things capabilities, must be part of a holistic, end-to-end strategy across the enterprise:

“Real-time data means removing the friction and latency from sourcing data, processing it, and enabling more people to develop smarter insights. Better decisions come from people trusting that the data reflects evolving customer needs and captures an accurate state of operations.” — Isaac Sacolick (@nyike), StarCIO Leader and Author of Digital Trailblazer

“Organizations must use a system that draws information across integrated applications. This is often made simpler if the number of platforms is kept to a minimum. This is the only way to enable a real-time, 360-degree view of everything that is happening across an organization — from customer journeys to the state of finances.” — Sridhar Iyengar (@iSridhar), Managing Director, Zoho Europe

“Streaming processing platforms allow applications to respond to new data events instantaneously. Whether you’re distributing news events, moving just-in-time inventory, or processing clinical test results, the ability to process that data instantly is the power of real-time data.” — Peter B. Nichol (@PeterBNichol), Chief Technology Officer at OROCA Innovations

As your data increases, expand your data-driven capabilities

The volume and types of data organizations collect will continue to increase. Forward-thinking leadership teams will continue to expand their ability to leverage that data in new and different ways to improve business outcomes.

“The power of real-time data is amplified when your organization can enrich data with additional intelligence gathered from the organization,” said Nichol. “Advanced analytics can enhance events with scoring models, expanded business rules, or even new data.”

Nichol offered the example of combining a customer’s call — using an interactive voice response system — with their prior account history to enrich the interaction. “By joining events, we can build intelligent experiences for our customers, all in real time,” he said.

It’s one of the many ways that new technologies are increasing the opportunities to use real-time data to fundamentally change how businesses operate, now and in the future.

“As businesses become increasingly digitalized, the amount of data they have available is only going to increase,” said Iyengar. “We can expect real-time data to have a more significant impact on decision-making processes within leading, forward-thinking organizations as we head deeper into our data-centric future.”

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

Business Intelligence

With so much diverse data available, why do so many companies still struggle to embrace the real-time, data-driven decision-making they need to be more agile? One thing is clear: The challenge isn’t solved by technology alone.

“You can’t buy transformation,” says Tom Godden, Principal Technical Evangelist with the Enterprise Strategy team at AWS. “Real change doesn’t come just from new technology—it comes from rethinking your processes, which are enabled by the technology.”

Godden offers three tips to help organizations break down data silos, improve data quality, and overcome other longstanding data challenges to foster a culture of decision-making that drives business agility.

1. Build the foundation for managing data in real-time.

A modern data strategy must emphasize data quality at the source of origin, rather than traditional methods of cleansing and normalizing at the point of consumption. Make sure you have the proper infrastructure, tools, and services in place to capture data from a variety of sources, ensure the quality of the data you’re collecting, and manage it securely, end to end.

The technical underpinnings of a modern data strategy include cloud-based databases, data lakes, and data warehouses; artificial intelligence and machine learning (AI/ML) tools; and analytics. The infrastructure must be supported by a comprehensive plan to manage, access, analyze, and protect data across its entire lifecycle, with fully automated processes and robust integration to make data actionable across the organization.

“It may sound obvious, but if you do not build the right processes to capture all the data, you can’t act on the data,” says Godden.

2. Don’t just democratize data – democratize the decisions based on that data.

Investing in the data management infrastructure, tools, and processes necessary to capture data in real-time through a variety of data feeds and devices is just the first step. If you aren’t simultaneously creating a culture that allows people to act on data, you’re just creating frustration.

To that end, avoid “reporting ghost towns” that require people to stop what they’re doing and access a different tool for insights. Instead, build analytics capabilities directly into their workflows, with context, so they can easily apply the insights to their daily activities.

3. Provide the types of guardrails that spur innovation instead of inhibiting it.

Building automated processes for metadata, including information on data lineage and shelf life, builds confidence in the data. By storing data in its raw or native format, you can apply access policies to individuals without having to modify the data.

This approach ensures more flexibility for how people can use the data they need without compromising the fidelity of the data itself. A data lake can serve as a foundational element of a data unification strategy, providing a single source of truth with supporting policies for real-time provisioning based on permissions.

Agile decision making: How three companies are benefiting from a modern data strategy

Organizations are already capturing the benefits of real-time access to data based on roles and permissions. Here are three examples:

Swimming Australia, the nation’s top governing body for swimming, has long been at the forefront of science. Now, it’s using data to analyze race performance and create bespoke training programs for individual athletes. A data lake unified athlete statistics and metrics in a single location, and AI/ML tools are helping the team tailor training programs and track competitors. Analysts and coaches capture real-time physiological data during training sessions and combine that information with race analysis to determine how to evolve training efforts for individual swimmers. Coaches and athletes can easily track progress in real time from their phones via cloud-based dashboards. Today, with its modern data architecture, the national team can create benchmarking reports in minutes, an innovation that helped make the Australians the most successful relay team in the 2020 Tokyo Olympic games.

Coca-Cola Andina, which produces and distributes products licensed by The Coca-Cola Company within South America, needed a solution to collect all relevant information on the company, its customers, logistics, coverage, and assets within a single accurate source. The answer was a cloud-based data lake, which allowed the company to implement new products and services to customize the different value propositions for its more than 260,000 customers. With all the resources and functionality that the data lake enables, Coca-Cola Andina ensures its partners and customers have access to reliable information for making strategic decisions for the business. Coca-Cola Andina ingested more than 95% of the data from its different areas of interest, which allows it to build excellence reports in just a few minutes and implement advanced analytics. The cloud infrastructure increased productivity of the analysis team by 80%.

Vyaire, a global medical company, needed a way to help its 4,000 employees make better, data-based decisions utilizing both first- and-third-party data. Adopting AWS Data Exchange to find, subscribe to, and use third-party data has made it easier to incorporate data sources into the company’s own data ecosystem, resulting in quicker insights to help teams focus on getting results, not administration. Easy access to third-party data via the AWS Data Exchange catalog has encouraged more experimentation and innovation, giving Vyaire’s leadership confidence that it can meet the changing market for respiratory care products and direct investment in the right area to improve its product portfolio.

Too many organizations continue to be held back from using data effectively to drive all aspects of their business. A modern data strategy will empower teams and individuals, regardless of role or organizational unit, to analyze and use data to make better, faster decisions – enabling the sustainable advantage that comes from business agility.

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

Digital Transformation