Data is one of the most valuable assets in an enterprise, yet transforming that data into insights causes so many challenges, its true value is often difficult to realize. These challenges also make it difficult for enterprises to optimize their technology investments.
Think about it: without data, most current technologies like customer relationship management (CRM) applications, enterprise resource planning (ERP) and customer engagement applications will fail to achieve the goals they were designed to achieve. And analytics applications are only as good as the data they are fed.
In Gartner's recent survey of more than 3,000 CIOs, respondents ranked analytics and BI as the top differentiating technology for their organizations, with top-performing CIOs calling it the most strategic technology area.
As a result, data and analytics leaders are increasingly implementing self-service capabilities to create a data-driven culture throughout their organization. The self-service approach aims to make it easier for business users to use and benefit from analytics and BI tools in the hopes they'll drive favorable business outcomes in the process. But does that approach always work?
"If data and analytics leaders simply provide access to data and tools alone, self-service initiatives often don't work out well. This is because the experience and skills of business users vary widely within individual organizations. Therefore, training, support and onboarding processes are needed to help most self-service users produce meaningful output,” Carlie J. Idoine, research director for Business Analytics and Data Science at Gartner, said in a statement about the research.
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Why Data Initiatives Fall Flat
The implications are clear. Data and tools alone are not enough to produce successful business outcomes.
Chad Dvoracek is senior data engineer at Chicago-based digital consultancy The Nerdery. He said companies fail to create significant transformational impact in data and analytics for a number of reasons, but these factors may be hard to quantify. Rather, the answer may lie in identifying the common traits of companies that have found success: leadership, culture and strategy.
“Digitalization, the process leading to digital transformation, is not strictly about technology. It is about creating change and using technology to enable this change and realize new opportunities. Implementing the latest and greatest technology in data and analytics won't transform a business on its own. Like any change process it requires strong leadership and vision, fostering a culture of change, attracting and empowering talent, ability to manage risk, significant investment, and clear strategic goals,” Dvoracek said.
Data takes a leading role in digitalization, but without the right organizational structure and leadership, many data initiatives become high-cost, low-impact ventures. Successful companies realize the potential of technology by leading a culture of innovation. They can inspire and empower teams to both fail and succeed in exploring new potential.
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Speeding Time to Insights With Data Lakes
Organizations must also remember what their priorities are based on, where they spend their time, and should align with multi-use solutions and easy-to-support technology, said Stephen Moore, chief security strategist at Exabeam.
“Data lakes are one such technology, providing data warehouses because they are designed to store large amounts of data in native form. This data can be structured, semi-structured or unstructured, and include tables, text files, system logs, and more. The idea is to support multiple use cases of the same data, including information security, as well as the ad-hoc approach of today's data scientists,” he said.“This provides a holistic approach for data scientists to evaluate data and extract valuable insights because of their faster, simpler approach. “
Fresh Consulting is a digital agency that works with enterprises struggling to create strategies around their data. Jeff Dance, CEO of Fresh Consulting, said it's not about the data in the end, it's about the information, knowledge and insights derived from it that makes it powerful for a business. Not enough energy is spent on the latter three. He identified the three stages data goes through to produce insights:
- Cleaning: The data is filtered and analyzed to ensure it's trustworthy.
- Data outputs: The second stage involves setting up and designing data outputs, dashboards and KPIs to support decision making.
- Proactive alerts: The third state creates proactive alerts and workflows, and various forms of internal and external benchmarking that can build on itself and derive wisdom.
AI Is Changing Analytics as We Know It
Ketan Karkhanis is SVP and general manager of analytics cloud at Salesforce. He pointed out analytics have been around in businesses for over 25 years, but that artificial intelligence is changing how we work and interact with analytics today.
He said analytics and customer engagement is no longer just about data, numbers, predictions and scores — it's also about recommendations and explanations, which lead to trusted transparency. A business user won’t trust recommendations if they don’t know why the AI is suggesting an action.
“At every customer interaction, every point of decision making, and every business process, you need the answers to four questions: What happened, why did it happen, what is likely to happen, and what should I do about it. If you’re not answering all those questions, you’re not using data to the fullest,” he said.
“When organizations consider analytics solutions and [data], they still tend to look at it from a systems viewpoint rather than a customer standpoint. When an organization’s leader asks 'why are we doing data analytics,' it shouldn’t just be to have a better data warehouse strategy. Leaders need to be thinking much bigger — how to improve business metrics such as margins, increase visibility, shape winning behaviors.”
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Data Doesn't Equal Insights
According to Rephael Sweary, co-founder and president at WalkMe, a San Francisco-based digital adoption platform provider, the real problem is defining what you are trying to achieve with data.
While organizations recognize the benefits of having real-time data to inform their business decisions, many enterprises are confusing data with insights. Some businesses will only look at whether the technical features of the software can increase productivity, but more savvy organizations will assess user behaviors over time to create personalized, actionable experience that drive a better outcome.
“Despite the influx of data points being collected in the workplace, defining the metrics for meaningful insights on employee productivity is still a challenge,” Weary said. “But as organizations of all industries and sizes focus on digital transformation, CIOs and business leaders must look not just at handing employees the latest tools, but at how to aggregate data to anticipate their needs to prevent a negative user experience or churn. In short, organizations are slow to adapt as this change requires a significant paradigm shift in work culture.”