Although the issue of silos in IT and data management are well known, companies appear to be falling back into this trap by not distributing their artificial intelligence (AI) and machine learning (ML) capabilities across their business. New research from Qlik and IDC revealed that just 20 percent of businesses widely distribute these capabilities across the organization.
However, with rise of analytics solutions that leverage AI and ML to augment users’ experience of and insights from data, many business leaders are recognizing that having these capabilities siloed in Business Intelligence teams will prevent them from generating the greatest value from their data. In fact, 42 percent consider expanding the use of AI and ML amongst workers as critical to improving the success of data analytics projects.
Three Sources of Silos
So, why are these silos arising once again? There are three key reasons, which many data leaders will be painfully familiar with.
The first is that many companies have gatekeepers to data across the organization. This, in and of itself, is not an issue as this approach often provides the simplest option for governance by keeping data secure. However, it does limit the opportunity of certain areas of the business to take advantage of all the data they need to run advanced analytics tools that incorporate AI and ML to augment users’ intelligence. As such, there needs to be a better balance between meeting the needs of IT and the business.
The second challenge is that there are some sources where it is difficult to get the data out – or where if you don’t do it in the right way, the data isn’t as useful as it should be. ERP systems, like SAP, are a prime example of this and limit the ability for business functions, like Sales, to incorporate its data into intelligent analytics solutions for predictive modelling.
Finally, many companies don’t have the skills widely dispersed across the organization to support a more democratized use of AI and ML. Research from Qlik and Accenture previously revealed just 18% of employees globally report that everyone in their organization has the skills they need to read, work, analyze and argue with data proficiently. Without these core data literacy skills, many knowledge workers will be unable to question and challenge the insights from intelligent solutions.
Democratizing the Benefits of AI and ML
in Data Analysis
Understanding the issue is halfway to solving the problem. Those IT and data leaders that take affirmative steps now can break down these silos, so that their entire organization has the potential to drive Active Intelligence from its data.
But, how can businesses successfully overcome the aforementioned challenges and increase the use of intelligent insights across their whole organization?
Beware of Silos – Again
Dismantling embedded, legacy silos continue to pose challenges to IT and data leaders the world over. As organizations embark towards a future of more intelligent analysis - with AI and ML enabling more proactive, personalized and collaborative experiences of data insights - these same leaders must ensure that they don’t fall into the trap of silos again. Democratizing the benefits of augmented analytics will not only improve the experience and outcomes of organizations’ analytical projects today, but will lay the foundations for more lucrative insights that will drive truly Active Intelligence in the future.