From BI to DI: Making Sense of Data

In comparison to previous years, business leaders are having to contend with an unprecedented volume of information, economic volatility, and global competition. Disciplines such as Data Analytics, Business Intelligence (BI) and Artificial Intelligence (AI) have been touted as tools for better decision making in order to gain a competitive advantage – with many organisations developing BI teams and Centres of Excellence (COEs) dedicated specifically to this.

Do these techniques always translate into the desired business outcomes?

For most businesses, extracting?insights from data?is inefficient?and costly, simply because of the volume of available data and indecision on which data and insights to focus on. In fact, research shows?that 20% or less of an organisation’s business data is actually relevant to achieving the strategic outcomes they desire, yet, many organisations take a blanket approach, treating all business data as more or less the same.

Analytics teams spend a considerable amount of time, effort, and money on massaging data in search of insights - many of which are irrelevant to making critical decisions or which miss the opportunity due to the time lag between insight and action. Until recently, there hasn’t been an explicit method to consistently bridge the gap between data and?outcomes at the organisational level.?

Enter decision Intelligence?(DI), a new discipline that brings humans and technology together. It is also one of Gartner’s top technology trends for two years in a row, with predictions of it becoming a $36B market in the coming decade.?

Why Decision Intelligence??

DI?is the natural extension of BI and AI in practice, providing a framework to effectively apply technology to executing the most complex and impactful business decisions. Instead of the ubiquitous data-centric approach of the last decade, DI starts with desired business outcomes and works backwards toward data and AI. It begins with people, not technology. It is still people, after all, who must ultimately make and be held accountable for high-impact business decisions.

DI tools and platforms – such as Sales Recommendation Engines (SRE) – enable organisations to simplify data reporting, focusing on the data which is most relevant and impactful for the desired business outcomes (which have been defined upfront). SRE’s, like Keystone, convert data into ACTIONABLE insights – quantifying the value of the sales opportunity in real time and providing alerts via an advanced ticketing system so the relevant individual can act immediately.

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Want to find out more? Watch here:

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Or contact us to schedule a demo for your organisation: https://meetings-eu1.hubspot.com/tennent

Content credit: Astral Insights (Chris Andrassy)