With the number of available data sources growing every day, the problem of data overload is also on an increase. To align your business model with the goals and objectives of the organization, you need to make data insanely useful. And, this is not done by merely generating huge data sets. Interpretation of the generated data is what is crucial for success, more so if it can be done in ‘real-time’. Also, insights that merely answer questions are not as valuable as the ones that help you move forward in a new direction by challenging the entire thought process behind a particular decision. This is what we call as actionable insights and maximizing these kinds of insights will maximize your data-driven success.
As a basic concept, terms like data, insights, and information cannot be used synonymously. The factual material in the recorded form is what data is. It can be either quantitative or qualitative and mostly finds its place in spreadsheets and databases. Data visualizations and reports that are generated from raw data deliver what we call as information, a more processed, aggregated and an organized form of data. This is more understandable and provides more context. Information in turn, when analyzed, leads to conclusions in the form of insights. These insights yield advantages that are more competitive.
The entire scenario can be better understood by a hierarchy pyramid with data forming the base of it, information as the next step and insights as the apex of the pyramid. Insights will take a company in a direction where it wants to go by finding new customers, improving customer retention and service, tracking social media engagement effectively and improving upon sales and marketing efforts. Actionable insights, on the other hand, will help improve business processes by cutting down on inefficiencies relating to money and time leading to achieving a better return on investments.
So now that a clear distinction between insights and actionable insights has been defined, we next move on to the parameters or attributes that decide how actionable a given set of data insights are. These are alignment, context, relevance, specificity, novelty, and clarity.
Strategically aligned insights based on an organization’s key performance indicators will lead to responses that further the effort of attaining business goals in an effective manner. The context in the form of background and benchmarks will lead to favoring actions more than unnecessary questions. Sending the right insights to the right people at the right time will reduce the level of subjectivity related to their relevance and lead to proper actions. Also, an insight is considered to be actionable only when it can properly describe with details as to why a particular event has occurred. Curiosity and not a set pattern is what drives evolution in knowledge and the more novel the insights are, the more it will drive people to venture into new areas. Lastly, the most important aspect is that of clarity, communicating effectively as to why and how the insights can help the organization.
A balance between traditional databases and big data solutions should be brought about. This unification will lead to a better control of collection, management, and analysis of data. Now, sophisticated data collection and analysis through blockchain and IoT is enabling to generate actionable insights to build competitive advantage. Most people who say that they ‘use analytics’ are really just looking at reports. Reporting and analyzing are two different aspects. In brief, finding insights and taking action based on it includes:
• Asking questions and finding their answers in the data generated.
• Hypothesis formation and testing leading to measurable results in the data.
• Understanding what are the lines and numbers that come up and then basing actions on that understanding.
An organization’s digital success depends on two factors – user experience and visitor expectations. To manage, analyze and improve upon these two factors, the key is to think along the lines of actionable insights and not just data. This means you have to combine your internal business experts with the expertise of external analytics specialists.