When it first surfaced a few years ago, Big Data was lauded as a goldmine of opportunity. A wave of analytical tools hit the market promising razor-sharp insights from endless silos of unusable data. Sure enough, most business intelligence tools today have reporting and analysis embedded in their offering, and there are hundreds of them to choose from.
But it’s not clear that these tools are delivering the rich insights we were promised. Are businesses actually making revenue-driving decisions on the back of Big Data, or is intense hype masking deeper issues?
Data, Data, Data
Access to data and metrics has increased exponentially. Users now have access to data that was inconceivable a few years back, theoretically enabling them to make better business decisions.
For example, marketing teams can quickly and effectively perform A/B testing for email subject lines. Based on instant feedback, the team can tweak their message or offering to optimise their campaign with minimal disruption. The faster the process, the better the return.
But when it comes to business-critical questions, like ‘Why has revenue dropped 15% this week?’, most tools fall far short of answering.
Information overload or operational inefficiency?
Businesses today maintain a long list of applications and tools in their tech stack. According to Siftery, Uber uses 268 different tools – slightly more than Apple’s 260. That is a lot of data to combine and analyse. Since reporting and analytics are not the core offering for most of these applications and tools, users are forced to manually export the data, manipulate it, and then import it back into dashboards and perform analysis to extract insights.
Given the quantity of data involved, this becomes a time-consuming and laborious process. Worse, data regularly passes through multiple teams and undergoes various transformations simultaneously. By the time these ‘insights’ reach the decision maker, they are often conflicting and out-of-date.
This is a wasteful use of tools, data, and resources. Because these data preparation tasks are so energy-intensive, they regularly consume up to 80% of analysts’ time. With too little room for useful analysis, the ‘insight-driven business decisions’ promised by Big Data remain lost, stranded in a murky sea of shapeless data.
Alignment of Talent
To help navigate this unruly sea, a new role was born: the data scientist. This is someone who can apply scientific methods, algorithms, and systems to extract knowledge and insights from data. The problem is that despite increasing analytics demands from management, few teams have access to a data scientist’s skills.
This leaves untrained staff attempting to quantify things like the effectiveness of a regional sales team, or to provide performance analysis of a recent marketing campaign. Unable to reach the crucial insights demanded by the business, this quickly becomes frustrating and costly for all involved parties.
Integrating data with action
Now having access to all this data, the next logical step is integrating with business intelligence (BI) tools which can derive accurate, actionable insights automatically. But with many to choose from, what makes a great BI tool?
The core functions of any BI tool are to: easily ingest data, perform transformations, produce visualisations, and above all, to generate insights. Most BI tools fall short of this final point, arming teams with attractive graphs and organised data but depriving them of actionable conclusions.
To be clear, insights are different from results. Any business user can examine Chart A and see that Product X performed well last week. The insight is articulating why the curve is trending upwards, and explaining the root cause of both the positives and negatives in business operations.
Quality BI tools need to be more than trendy dashboards and attractive visualisations. They must leverage data and provide actionable insights to users, guiding decision making. To truly utilise Big Data and leverage its transformative powers, business intelligence needs to step up.
A Clearer Future
Today, we are witnessing a new wave of business intelligence tools which use machine learning and artificial intelligence to provide these sought-after insights. Even better, users can be alerted about unusual behaviour against KPIs in real-time, while the tools sift back through the data to determine the root cause of any anomaly triggering an alert.
Thanks to machine learning and AI, analytical tools have now become the game-changing technology we’ve been looking for. They are vessels which can guide analysts through this vast sea of data, and finally bring home the rich rewards that were promised.
Want to see how AVORA can help your business making better decisions with your data? Get in touch to arrange a demo.