Today organizations are awash in data. CxOs recognize that their organization’s ability to generate actionable insights from data, often in real-time, is of the highest strategic importance. Although business leaders know that data has immense strategic value, many fail to take full advantage of data analytics to stay ahead of changes in their business, properly diagnose why changes occur, and determine how best to improve performance.
Data analytics can’t produce benefits unless people use them. For many enterprises, the investment in data science falls short of expectations because effectively turning their analysis into action remains elusive. There is a gap between what the data scientist produces and what a business user needs to make decisions on a daily basis.
Barriers to Producing Business Value from Data Science
Too often, the relationship between your data scientists and the rest of your company is characterized as “misaligned”, “contentious”, or “disconnected” — or at the very least suboptimal. Neither fully understands the other, leading to a stalemate where you feel you aren’t getting the research you need or knowing what to do with the data that your data science team provides. Likewise, and as importantly, your data scientists feel that their hard work is going to waste.
Here are a few reasons this is so prevalent in the workspace.
It’s difficult for business decision-makers and data scientists to communicate on common ground
That’s because the roles are vastly different despite having a common goal (your company’s performance). Data scientists spend all day managing vast sums of data, building models, employing machine learning, and trying to uncover insights. Their job is to analyze, experiment, and try to get the data to tell a story.
The rest of the business, on the other hand, is concerned with driving metrics through strategies and innovation. They want to boost profits, streamline processes, and make informed decisions.
Ideally, the data scientists provide the information and insights that your business needs to act on these goals. But what decision-makers want from the data is simple – they want insights AND recommendations to improve outcomes. They are far less interested in dashboards, graphs, charts, and the storyboard. They want a clear and actionable plan to improve key metrics.
The result is a team of data scientists who aren’t sure how to present their findings to the rest of the business. They have all of this information but, not fully understanding how you can leverage it, cannot express the usefulness of what they have uncovered.
More often than not, 58% of the time, according to a recent Gartner survey, business decisions are made without respect to the data. And how often is data used to justify a decision instead of driving the decision in the first place? So much for data science providing data-driven results. Each side blames the other. Business leaders claim that they do not get what they need to make effective and timely business decisions. And the Data Science team feels that the business is not using the result of their labor.
Data vs. Recommendations
Another point of contention between data scientists and business decision-makers comes from the differences between research and recommendations.
Too often, your team is being presented with data by their data science peers. It sounds big and exciting in the Zoom meeting, and you’re sure that it’s going to lead to a lot of ideas. Once you leave the meeting and sit back at your desk, however, you realize you have no idea what to do with the data you have been given.
That’s because what you need are recommendations. Yes, those recommendations should be based on research, but the data should explicitly point out what action you need to undertake to improve the business. This is especially true if you have a limited understanding of the research your scientists have presented you with, which isn’t uncommon.
Data analysis is time consuming and challenging
Lastly, data analysis is a tedious and challenging process. It takes a lot of time to wrangle the data, build and tweak the models, and, of course, the world is dynamic and dramatic shifts (say like a pandemic) often mean that the process must be reworked.
Once you get the output from your data, it is typically in the form of a series of charts, graphs, and dashboards. This is what is commonly and ironically referred to as business intelligence (BI). These elements need to be analyzed to try and uncover insights, and then the insights must be correlated and synthesized. Then an analyst needs to apply business context to the insights, and then, after all this manual effort, perhaps a thesis can be formed and eventually a decision reached. More often than not, there is no time nor resources available to pursue this manual effort. Hence decisions are made, but they are not data driven.
How Decision Intelligence can bridge the gap
Fortunately, there is a solution: Decision Intelligence (DI).
For those not in the know, decision intelligence is a solution that turns your data and your data science into direct, recommended actions — and it’s what Diwo’s Decision Intelligence Platform delivers. Decision intelligence accelerates the path from data to decisions. It helps users quickly evaluate different scenarios and outcomes so they can make decisions and take action when it matters most – in the business moment.
Contextual Intelligence eliminates “analysis paralysis”
Diwo is not a data science platform. It is a decision intelligence platform that sits on top of your existing investments in your data fabric, data warehouses, and AI\Machine Learning models. Decision intelligence consumes the output of your data science team’s models. Diwo eliminates manual analysis effort by automatically surfacing, correlating, and synthesizing all insights. We then apply your business context through our use of contextual intelligence. The business users see an opportunity to improve, the potential value of the improvement, and a specific, actionable recommendation. The user double clicks on the recommendation to view the “evidence” which explains the data and logic behind the recommendation. They validate the recommendations and then take action.
It’s focused, fast, and efficient.
NLP lets the data speak for itself
Diwo’s Decision Intelligence platform leverages Natural Language Processing (NLP). This is functionality that allows you to communicate directly with the AI that’s managing and analyzing your data in your natural language and is very intuitive.
You can hold a conversation with your data analytics, asking questions just like you would another person and getting direct and digestible answers. This makes life much easier on the data science team as well because the available data does not require anyone to engage further to make it consumable.
Data Scientists Deliver Business Results
Decision Intelligence fundamentally changes the business vs data science dynamic by using AI and contextual intelligence to enable data scientists to deliver an actionable recommendation immediately to the business user. Data Scientists don’t need to change their data or their models in order to improve productivity. Business users are empowered to make data-driven decisions multiple times daily. Decision intelligence fulfills on the vision of what business users were expecting from their investments in data science and vice versa.
Transform your business with Decision Intelligence
In today’s dynamic environment, decision-makers need to act quickly with full visibility into the impact of their decisions and actions. Across industries, data-driven organizations are turning to Diwo’s Decision Intelligence platform to evolve their analytics tool kit and empower decision-makers with insights and recommendations that drive action and immediate business impact.