Debunking the Myths of Data and Analytics

Analyst Viewpoint

Business analytics doesn’t have to be a mystery, yet there are many myths that persist about how to become a data-driven organization. In the age of disruption, organizations need to have access to all of their data, and they must be able to apply a wide range of timely analytics. To do so can be challenging. The lack of reliable data, control challenges and concerns around governance and security of data are complicated. The evolving nature of data and the time-consuming process of collecting, validating and reconciling data leaves less time for strategic, value-adding activities and analysis. Organizations are still too dependent on manual planning in spreadsheets. Some may not be unable to link operational and financial performance or optimize resources across departments due to disconnected processes and siloed data. But with the appropriate technology and processes, these challenges can be overcome.

It is vital to get information into the hands of those that need it to operate an organization effectively and efficiently. Despite cries that “dashboards are dead,” much of an organization’s information is still delivered via reports and dashboards. In our Analytics and Data Benchmark Research, more than 8 in ten participants (87% and 83%, respectively) indicated reports and dashboards are important to their organizations. Other types of analytics are important as well. Adoption of artificial intelligence and machine learning (AI/ML) and natural language presentation continues to grow, with more than one-half of organizations already using or considering using those technologies (59% and 56%, respectively). Unfortunately, the skills needed to take advantage of these newer technologies is in short supply among workers, so well-established analytical techniques such as reports and dashboards aren’t going anywhere any time soon.

According to our research, less than one-quarter of organizations (23%) have the AI/ML skills they need to successfully use data. Not everyone must be a data scientist to take advantage of new technologies, however. Tools that provide automated insights using AI/ML deliver fast, reliable analyses that increase revenue, decrease cost and enable organizations to gain a competitive advantage without specialists doing all the number crunching. The results of these automated analyses can also be distributed throughout the organization using reports and dashboards, further extending the reach of AI/ML without requiring specialized skills.

Predictive analytics based on AI/ML are not the only way for organizations to see into the future. Data architectures that include driver-based planning can improve forecast accuracy, speed up planning cycles, and provide better insight and visibility into potential outcomes. Planning capabilities are critical for evaluating the future impacts of decisions and alternative scenarios. A driver-based planning process can project the organizational impact of different scenarios to deliver accurate, informed forecasts and operational plans. With proper planning, organizations can be proactive rather than reactive, helping leaders make better decisions by anticipating the future.

Analytics should consider the past, the present and the future. It is important to understand the past, learn from it and prepare the appropriate responses based on the observations made. As pointed out above, planning supports decision-making, but many decisions need to be made here and now. While real-time decision-making can be challenging, automation, streaming data and analytics, and AI/ML enable organizations to respond in the moment. Customers expect and demand real-time responses, and organizations are stepping up to the plate to provide enhanced customer experiences. More than one-fifth (22%) of organizations are responding in real time today, and more than one-half (52%) believe they currently have adequate real-time capabilities. An information architecture that supports streaming data makes it possible to respond almost instantly.

With these various data and analytic requirements, one might think it’s impossible to eliminate data silos. However, implementing a data fabric architecture across the entire organization provides ready access to all the necessary data with up-to-date, high-quality, trusted data whenever and wherever needed. A data fabric that includes data virtualization capabilities eliminates silos, increases self-service access to data and improves satisfaction with analytics. Every department has access to all the data needed to make informed business decisions. Data fabrics also break down organizational and process silos, such as disparate spreadsheets and unlinked dashboards, to enable faster data gathering and analytics. Within a data fabric, all data across planning, analytics and operational applications share the same governance rules and security protocols.

For organizations to be effective and to be competitive in their markets, they must have a strong data and analytics foundation in place. On the data side, end-to-end data management, governance and self-service access to any and all data will ensure consistency and accuracy in operational decision-making. A breadth of analytics ranging from the ubiquitous reports and dashboards to AI/ML-driven insights and driver-based planning will provide the visibility needed to successfully navigate rapidly changing markets. Business and IT leaders should look for a platform that dispels some of the common myths about data and analytics, streamlining the process of becoming a data-driven organization.