Combining Analyst and Machine Power to Drive Business Results
If you’re a data analyst, you’ve probably been approached by company stakeholders asking you questions like: Why is revenue down? Which customers are most likely to churn? What are my top channels to acquire new customers? Why is my business losing more orders in rural areas?
Data analysts know the answers to these questions lie somewhere within their ever-growing troves of company data. However, stakeholders often don’t understand the complexity inherent in answering these questions, particularly when dealing with data at cloud scale. In many cases, answers to important business questions are revealed days or weeks later, slowing down decision-making processes and affecting the business’s bottom line.
According to a recent report from McKinsey:
“Many business problems still get solved through traditional approaches and take months or years to resolve. By 2025, nearly all employees [will] naturally and regularly leverage data to support their work. Rather than defaulting to solving problems by developing lengthy, sometimes multiyear, road maps, they’re empowered to ask how innovative data techniques could resolve challenges in hours, days or weeks.”
As the modern data stack continues to evolve, the amount of data companies collect continues to increase. This progression in data volume, variety and velocity ushers in a new challenge: combing through all of the available data to generate business value.
A recent Gartner report revealed, “The volume and velocity of data and increased complexities in decision-making have become too much for a human being to handle without assistance.”
So what is the answer? Putting the power of automation in the hands of data teams.
Data teams are starting to understand that operationalized machine learning-powered analytics can increase efficiency and eliminate rote data science work. The ability to rapidly process cloud-scale data, separating signal from the noise with pre-built and operationalized artificial intelligence/machine learning tools, is a necessity for analysts in today’s complicated data-rich era.
Analysts today are bottlenecked by tools that mandate the time-consuming manual analysis of data. Analysts spend days or weeks manually defining and testing hypotheses to identify the causal factors behind changing business performance. But it’s not the analyst who is at fault. Most analytics tools allow analysts to pivot dimensions against each other and to explore data and are very useful, but even as a seasoned analyst, you’re probably only able to test one or two hypotheses per minute.
When comprehensive, accurate analysis requires testing millions or billions of hypotheses, analysts often can’t respond in time to business needs. Further, analyst teams are forced by limited resources to prioritize the questions they answer, as they simply don’t have the resources to support all the decision-makers who require support.
Despite the challenges of scale and complexity, most organizations are able to understand changes that are happening within their data through traditional BI tools. However, most don’t realize manually tracking what happens to metrics is only the first step in the decision-making process.
Strong data-backed decision-making doesn’t stop after learning business status (what is going on) because what doesn’t tell us why it is happening or how to go about addressing it (what next). Understanding and communicating why and what next is the sweet spot where human input and machine automation come together to drive value from data. Effectively answering what, why and what next relies on new ways of tying together people, processes and advanced technology into a single system: decision intelligence.
People are the keystone in the puzzle of getting value from data, particularly complex cloud-scale data. Machine learning and automated delivery of important facts are also only one part of the puzzle. A human has to take these facts and explore them against what is currently happening in the business.
Putting the power of machine learning in the hands of analysts by deploying decision intelligence tools allows them to quickly, proactively and automatically iterate upon the what, why, and what next to quickly and efficiently determine how to prevent issues like customer churn or take advantage of opportunities like the best channels to acquire new customers.
Tools like the Sisu Decision Intelligence Engine help companies wherever their data is housed, whether it be a warehouse or metrics store, and answer those tough questions on what, why, and what next to optimize business performance.
If your organization is looking for a more efficient way to leverage its data to drive business impact, it is important to remember that adding a decision intelligence tool to your tech stack does not replace your BI tools or data science team. In fact, decision intelligence helps data science teams by making them more efficient and helps data scientists focus on the most relevant areas of their data.
By automating the combing through all of a company’s trillions of data points to surface insights, data scientists are freed up for more strategic, less repetitive work. A decision intelligence tool is meant to supplement data efforts by performing hypothesis testing at a massive scale and at a fraction of the time of humans alone.
Decision intelligence augments existing BI and data science processes to improve efficiency and feed teams with insights that matter the most to present what, why, and what next through existing interfaces.
Decision intelligence helps organizations drive business outcomes by augmenting people with advanced analytics capabilities integrated directly into decision-making and operational processes. At Sisu, we believe that decision intelligence is what marries people, process and technology together, extracts the most value from data and drives transformational business change.