Just like marketers have struggled to find the correct answer to the question “what does the customer want?”, analytics leaders have struggled to find the correct answer to the question “how to build a data-driven organization?”. It is very clear at this point that the key is to inculcate the culture of data and analytics in the frontline of the organization, making them a breeding ground for citizen analysts and data scientists. But how to attain that objective has been a challenge. Millions have been invested in tools and technologies to become a “data-driven” organization. Though a majority may not admit it, most organizations are far from becoming one. Despite investing millions in “best-of-breed” tools. So is it not about tools and technologies? It is about the tool. It is the analytics tool that these frontline workers leverage that will determine if they end up embracing those tools as the most important arsenal in their tool chest. While analytics tools become more and more powerful and feature-rich, many still lack three key ingredients that we believe need to be embedded in a business analytics solution. These were also highlighted in our research report "The Future of Business Intelligence". These three ingredients are:

• Intuitive
• Interactive
• Integrated

In this article, we will review these three key ingredients an analytics tool needs to have to become a significant contributor in your journey to become a data-driven organization. Intuitive: Just like art, when you embark on an analytics initiative, you start with an end goal. That end goal (hopefully) is not an algorithm that successfully spits out numbers. That end goal, ideally, is a specific “presentation” of results in a way that makes it easy to ingest and digest. It could be a dashboard or a tab in a spreadsheet, but the goal is to ensure that it contains what will make someone’s life easier. That then translates into developing a data-driven employee. In the race to differentiate in a rapidly crowding marketplace, analytics tools are becoming fancier and, in some cases, more complex. To “showcase” that they can do many things, they typically spit out “too much” to the end user. And this is a challenge! The fact is, end-users, specifically those we want to convert to citizen analysts, do not care. What they care about is whether the data is in a form they would want to see. How they see insights needs to be aligned with their “intuition,” not in terms of what they expected to see (which may not always be the case), but how they expected to see it. Of course, they should have the ability to slice and dice that view, but the key is- is the presentation of insights intuitive to the end-user in a way so that they can digest the information immediately? If they can, they will eventually make it a part of their routine to leverage those insights to make decisions. Interactive: Analytics tools need to go beyond the vanilla interactive capabilities of filters and views. This is where the power of augmented analytics comes into play. As humans, we like interactions. But in the world of analytics, having the ability to interact with the tool works in multiple ways to drive the psychology of the end-user to embrace the tool. Suppose you can type a question or ask a question verbally to generate a specific view vs. navigate through a series of filters. In that case, it induces simplicity from an end-user perspective. It also gives the end-user the feeling of a two-way interaction and experiments with various analytics approaches without being an expert. Consider an example of product segmentation for profitability. A tool may use a certain algorithm based on the relative importance of attributes and present a segmentation to the end user. But the choice of the algorithm may change if the user decides to play around with either the attributes themselves or the importance of attributes. If they can type those “scenarios” to generate them without them being experts in underlying algorithms, it not only makes it engaging and fun, but it eventually becomes a habit. This is how employees become “data-driven.” Integrated: The reason enterprise analytics tools, like SAP Analytics Cloud,  have gained prominence is that you can not have a data-driven organization with siloed analytics. Irrespective of which function or team you work in, making an optimal decision today is about factoring in insights that go beyond the realm of your team. And that optimal decision is what employees in true data-driven organizations make. Hence, integrated insights are essential in a robust business analytics solution. Unless an employee is aware of the quantified impact of their decisions on the overall organization, specifically interfacing teams, the organization can not be considered data-driven.