By 2018 there will be direct monetization of IoT algorithms: Gartner BI, Analytics & Information Management Summit
BI and analytics market is changing and giving way to new business models. How to keep pace with it and how to master the data world were the questions answered at Gartner’s BI, Analytics and Information Management Summit in Mumbai.
It is a no-brainer that data dominates in today’s digital economy. However, organizations have not yet mastered the art of navigating through this complex maze of data in a strategized way. Kurt Schlegel, Research VP, Gartner kicked off the event by saying,”Organizations cannot collect data in one central place anymore so this makes it necessary to connect the data rather than collecting.”
Organizations want to know how to create best business results. For this, every data conversation needs to be business conversation. Not to forget that generating business value is team sport. Indian business intelligence (BI) software revenue is forecast to reach US$ 213.8 million in 2017, a 18.6% increase over 2015 revenue of $180.2 million, according to Gartner. This forecast includes revenue for BI platforms, advanced analytics platforms, analytic applications and corporate performance management (CPM) software.
“The BI and analytics market is undergoing significant change. Adoption of machine learning techniques, and the emergence of smart data discovery solutions are fueling next round of investments,” said Bhavish Sood, Research Director at Gartner. “We see signs of the emerging importance of BI in India as senior executives are increasingly exploring the different styles of analytics to resolve their business imperatives,” said Sood. “There is an increased emphasis on metrics management and the growing use of performance management. Big data use cases are maturing and have executive visibility. This is leading to more
investments in BI and information management.”
Shifting the focus on algorithms which are at the core of BI strategy Debra Logan, VP & Gartner Fellow said, “According to one of our surveys 85% of data strategies did not have quantified outcomes. Algorithms need to be governed and maintained to give quality. Predictive and prescriptive analytics has to go from control to influence. Better business outcomes comes from focusing on business outcomes of customers, partners and the entire ecosystem.” She also emphasized that business value is not just reduction in cost. The numbers need to trigger questions about business. The main goal should be to connect people through analytics for which we we need to be data storytellers. Presently, it is more than possible as selfservice tools are turning people into citizen data storytellers.
Taking political analogy Kurt Schlegel, Research VP, Gartner mentioned that presently, data analytics belongs to liberals. Virtually all analytics software purchases are beginning as a free or low cost POC according to Gartner predicts by 2017. Vendors are encouraging this by Freemium models and later utility based models. From liberal the journey continues towards anarchists with hundreds of vendors in this space. Anarchy will continue to disrupt. By 2020, 75% of midsize and large organizations globally will compete using advanced analytics and proprietary algorithms.
Schlegel mentioned that there are algorithmic markets out here where one can purchase them. Algorithms will be at the heart of digital economy. By 2018 there will be direct monetization of IoT algorithms. When talking about algorithms and ecosystem one needs to focus on the ‘trust’ factor. Trust is at the core of data revolution and a positive business outcome. How can organizations do better job of trust? According to Logan, the trouble with external data sources is its truthfulness. Even internal data sources are getting too voluminous. Organizations can take two approaches here. They can trust till there is a reason to distrust or distrust till there is a reason to trust. Trust and verify is the suggested way to go about it. Ask questions like is the source trustworthy? Does it offer meaning to measure quality? One can also use the approach of triangulation and see if there are more sources pointing to the same result or data? Have others found the data useful? Is there metadata that shows how this data is being used?
Another worry in the data world comes in the form of security and governance. Information governance is also a key component to building trust in the data world. It starts with a simple question i.e. who owns the data? Data ownership is a difficult challenge to resolve. Coming henceforth would be who owns these algorithms? Through 2018, only a minority of organizaton will have a rigorous approach to demonstrating the trustworthiness of there analytics algorithms. Lastly, speaking on Information security Schlegel and Logan mentioned, “Worry about data all the time. Be aggressive when it comes to all security and privacy related technology. Can you uniquely identify the person interacting with your data? That is the question to ask. Securtiy analytics is a hot new field which needs attention from organization.”
Food for thought at the event came in form of three tenets of analytics wisdom:
-Accept that the world of analytics will be more distributed.
-Shift thinking from truth to trust.
-Increase urgency of information security.
