By Brynne Henn - December 10, 2020
Most data executives find themselves trapped between two competing priorities. On one side, they face the tactical challenge of building an organization that can effectively translate vast amounts of complex data into clear business decisions. And on the other, they’re tasked with the strategic imperative to increase data literacy across the company, investing in a data-driven culture that can capitalize on the continuous feed of data insights generated from this data.
At the heart of this dilemma is the analytics bottleneck: With an infinite ability to capture and store data, it can now take days – and in some cases weeks – for even the most sophisticated analysts to find and process the relevant information, generate the hundreds of queries required to sufficiently answer a given question, and present the answer in an interpretable, actionable format.
To escape this dilemma and unblock this bottleneck, CDOs must put a stop to these five common problems in their business.
In theory, access to wider, fresher data managed in a cloud-native infrastructure should be the end of gut-feel decision-making. But, if a team is still relying on legacy BI tools to make sense of that data, that dream is far from reality. As your analytics team tries to answer requests with these manual tools, unanswered questions pile up — forcing business teams to continue as usual and make decisions based on best guesses and instinct.
It’s time to put aside these legacy processes and adopt new Augmented Analytics platforms that can deliver high-impact insights through faster, guided analysis of key metrics. With faster answers based on all the available data, you’ll break the analytics bottleneck and demonstrate the advantages of data-driven decision-making and stop gut-feel decision making once and for all.
Today, most organizations only use a small portion of their data to inform decisions, despite having access to more context than ever before. This is partially due to the proliferation of data silos across departments and applications, but it’s exacerbated by the fact that so-called “modern” BI tools were designed for simpler, smaller datasets housed in legacy on-premises systems.
When data is hard to access and aggregate, and the process of hypothesis generation and testing is manual, it leaves little time for creativity. Manual data prep and analysis slows down the process, bases insights on only a subset of your data, and puts handcuffs on deeper data exploration.
Augmented Analytics breaks down this analytics bottleneck. Built with complex, granular data in mind, Augmented Analytics uses all your available data to quickly surface insights on the factors, and combinations of factors, driving changes in your metrics. Instead of spending time transforming the wealth of data you’re collecting, your analysts can focus on what they’re best at — interpreting the results and diving deeper into the data.
The growing complexity and dimensionality of enterprise data creates new challenges for analytics teams: knowing what to investigate, where to look in the data, and how to focus their efforts. This problem makes it difficult for data teams to deliver consistent, complete, and high-quality analysis that answers more than the simple question of what changed in your data.
Some CDOs try to solve this problem by hiring more analysts to write queries, test hypotheses, and keep up with the business’ requests, but as the sources and complexity of the data continue to grow, this approach is expensive and cannot scale.
To start answering both what changed and why, Augmented Analytics makes more of each analyst’s time by automating the rote, time-consuming pieces of analysis and cuts down on the time analysts spend finding, prepping, and exploring data manually. Augmented Analytics then focuses the team’s attention on the metrics that matter most and tests every possible factor impacting those metrics, delivering detailed, complete analyses on both what changed and why.
Today, analytics teams are forced into a reactive mode, constantly responding to ad hoc data requests from the business, maintaining static dashboards for different organizations, and trying to answer requests as quickly as they come in. This cycle can build an anti-data-driven culture where analysts are treated as a mere report factory that writes queries to the data and provides access to the data across the business.
CDOs must invest in tools to augment and elevate the analytics team’s role beyond the “gatekeepers to the data” and towards a strategic business partner, which starts with providing the answers and context that enables stakeholders to act decisively.
Augmented Analytics is the key to making that shift. By providing faster answers to both what’s changing in metrics and why, analysts can interpret results and spend more time proactively finding the problems that need to be solved for the business.
If the rest of the business can’t make sense of the insights and context analysts deliver, companies can never be truly data-driven. So on top of everything else, CDOs face immense pressure to build a data-literate workforce that can take action based on data— whether they’re in the analytics organization or not.
The problem with this approach is people don’t want more access to the data — they want more answers. Trying to make everyone in the business an analyst won’t create a data-driven culture; it only creates more work for the company and shifts the burden to less data-savvy leaders.
Instead of turning everyone into an analyst, you need to start giving everyone direct access to answers using Augmented Analytics. Rather than creating more spreadsheets or point-and-click dashboards, or hoping business users start testing hypotheses for themselves, Augmented Analytics platforms like Sisu will stack-rank insights by relevance. This presentation offers easy-to-understand facts that focus on the metrics that matter most to the business, empowering every team to act confidently based on their data.
If you want to truly build a data-driven culture that impacts the business and increases its value, you need to stop looking to legacy BI tools and solutions to address the analytics bottleneck. These approaches will continue to lag behind as data volumes and complexity continue to grow. Instead, it’s time to adopt an augmented approach to analytics, enabling you to move from “what” to “so what” as quickly as possible. Reach out to our team to learn how.