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Debunking the Myths of Data and Analytics

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.


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David Menninger

SVP and Research Director
Ventana Research

David Menninger is responsible for the overall direction of research on data and analytics technologies at Ventana Research. He covers major areas including artificial learning and machine learning, big data, business intelligence, collaboration, data science and information management along with the additional specific research categories including blockchain, data governance, data lakes, data preparation, embedded analytics, natural language processing (NLP) and IoT.