TDWI Upside - Where Data Means Business

Where to Focus to Deliver Analytics Value

TDWI research finds most organizations want BI, analytics, and the underlying data management infrastructure to address these five areas.

Few topics excite business and IT professionals more than analytics. There's certainly hype; you can't walk through an airport without seeing the word analytics on book covers, magazine headlines, billboards, and even mentioned in television monitor news tickers as you wait to board your plane. Of course, analytics shares the spotlight with artificial intelligence (AI) and machine learning as related means of harvesting value from the torrent of data generated by humans, things, applications, and services.

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The excitement is good for all of us in the industry, but hype can be worrisome. Ultimately, executives and managers want to see value: that is, how does this improve margins, attract and retain customers, enable employees to be more effective, and push our firm ahead of the competition?

TDWI's Conference and Leadership Summit, to be held the week of August 5 in Anaheim, CA, will offer keynotes, case studies, and training sessions devoted to educating professionals on how to apply BI, analytics, and data management to deliver business value. It is important to think not just about how to develop analytics, AI, and machine learning programs but also about their business, ethical, and societal impact. Harry Glaser, cofounder and CEO of Periscope Data, will set the right tone with a keynote discussion about "the moral compass of AI": why organizations need to ensure that ethics is part of their strategy.

Ethics and governance are rising in importance because some perceive analytics and AI as galloping ahead of the "real world," so to speak -- that professionals involved in analytics and AI development see the value of their projects as self-evident and are blind to the larger impact. Ethics and governance address the societal and legal context, but business leaders also need to see that analytics and AI projects are more than just interesting for their own sake and actually deliver business value. Business leaders and users must understand how projects' value fits into the context of business objectives, problem-solving initiatives, and reputation in the marketplace.

Delivering Value: Where to Focus

It can be hard to determine the hard value of BI and analytics because it is usually found "downstream." The attention of executives and line-of-business managers is more often focused on immediate priorities such as closing a sale before the end of the quarter, putting the right marketing message in front of the right customers, or deciding how to optimize a supply chain. Data and analytics professionals know their technologies and practices can produce insights that potentially improve the chances of a beneficial outcome in all of these areas. However, if insights are not focused on the problem and communicated in the context of business decisions, the value will be lost.

Here are five areas that TDWI research finds most organizations want BI, analytics, and the underlying data management infrastructure to address. Solutions that score wins in meeting these needs will deliver value and receive sustained support.

Actionable insights. It might seem obvious, but there's a fair amount of frustration among users that the fruits of BI and analytics (such as reports, visualizations, and nuggets of data wisdom) are not relevant to their decisions or responsibilities.

Analysts, data scientists, and developers should take the time to learn the users' world. A good strategy is to literally live in their world and accompany them as they go through their days and try to solve problems. Find out what goals and metrics they're accountable for. What questions do they need resolved through analytical insights? Then, use data visualization to communicate the insights so users can more easily grasp their importance and apply them to decisions.

Single, integrated view. Often called the "single view of the truth," the longstanding goal of many BI and data warehousing systems has been to enable users to view essential information about customers, products, or other objects of interest through one system or service. It is a never-ending quest as data keeps changing and users want to analyze new information sources such as social media.

AI and machine learning are the latest technologies that solution providers are embedding in data collection, preparation, and integration tools. The most current wave targets the needs of analytics projects, not just BI; they offer technologies for evaluating the relevance of new data, spotting data relationships, and making it easier to see correlations across data sources. Focus on how you can provide users with analytics built on a comprehensive view of all the data.

Productivity and efficiency. Organizations are always looking for how their employees, processes, and operations can be more efficient and productive. Analytics can help personnel modernize processes and change their workflows to spend less time on tasks that software can automate, possibly through application of AI. They could then devote more time to value-adding activities. Look for opportunities to apply analytics to improving operational productivity and efficiency.

Speed. Enterprises highly value analytics that can reduce latency between the ingestion or streaming of data and production of analytics insights that can make a difference to the business. Companies in many industries compete on speed: who can make the best offer to a customer fastest or who can spot an opportunity in the market sooner.

Many organizations have been collecting terabytes, if not petabytes, of data, hoping that by mining it they will uncover such insights. Yet, as the data piles up in data lakes, hubs, and warehouses, many are still waiting. Find ways of producing insights faster. Examine technologies such as streaming analytics, data virtualization, and cloud analytics to shorten the path.

Communication and collaboration. In the end, if no one can articulate the results of analytics projects, you will lose value. Analytics and data science teams should be sure to spend time on how they communicate the results and the data lineage behind the analytics. So they can trust the results, users will want to know where the data came from and how it may have been manipulated along the way. They will want to know how the analytical models work (at a high level) and the potential impact of the analytics on the organization. Evaluate visual analytics tools for capabilities in data visualization, storytelling, and data lineage tracking.

Align and Innovate

Analytics can help users move beyond simple BI and performance management practices to understand more about the underlying data and contextual information relating to why key performance indicators may be out of line with expectations. Analytics is the stuff of innovation; insights generated from data can unearth reasons for performance results that BI reports and dashboards obscure, leading to new ideas for solutions. In this way, analytics can help organizations be smarter in aligning decisions and actions with strategies and objectives. With analytics, governance, and attention to ethics, the strategies and objectives can reflect the full scope of an organization's aspirations in business and society as a whole.

About the Author

David Stodder is director of TDWI Research for business intelligence. He focuses on providing research-based insight and best practices for organizations implementing BI, analytics, performance management, data discovery, data visualization, and related technologies and methods. He is the author of TDWI Best Practices Reports on mobile BI and customer analytics in the age of social media, as well as TDWI Checklist Reports on data discovery and information management. He has chaired TDWI conferences on BI agility and big data analytics. Stodder has provided thought leadership on BI, information management, and IT management for over two decades. He has served as vice president and research director with Ventana Research, and he was the founding chief editor of Intelligent Enterprise, where he served as editorial director for nine years.


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