Friday, February 20, 2015

The Big Story of Big Data

Storytelling is nothing new, it's the integration of the art with the science.

Big Data continues to emerge as one of the top priorities in executive’s agenda though data itself is not necessary fun, what’s the big story of Big Data? Storytelling skills will continue to grow in importance for analysts to be effective in 2015. But this isn't really new, perhaps it's just that more organizations are going through the learning curve of what they need from a balanced analyst (the artistic scientist or a creative analyst) to tell stories and articulate well to tailor the interest of the different audience.

Storytelling is nothing new, it's the integration of the art with the science. You really need both skills to tell a good Big Data story. This is a very important trend. It's been growing for sometime but is beginning to get the attention it deserves. As technology - in all areas - becomes less siloed, and an even more integral aspect of the wider business - the need to communicate the insights of data as a clear, actionable story becomes ever more critical. So Data professionals, need to focus as much on those communication skills as on the "hard" aspects of data science. It calls out the need for both understanding of subject matter and data to gain insights. Too many times these perversely are on different sides of a wall. For the data analyst to be a storyteller requires that they well understand the underlying real world issue being studied. It's a good data analyst who possesses not only the technical skills needed to extract relevant data but knows what it means and can then tell the story. They are rare and valuable.

The data is very important, but the interpretation of those data is equal of importance. We live in a world that wants to eliminate, or, at least, reduce risk through "hard" data. There's the implication that, with enough data, decision making could be almost risk-free. But the data is just the initial basis for the decisions. Decisions still have to be made in a human way, and human beings have always been moved most by strong stories. Data is critical. But it's just a first step, the data is a very important base, but the interpretation of those data is equal of importance. Just like the same news can be showed with different points of view by different readers, the Big Data can tell the different story from the different angle. Avoid pitfalls of mistaking data for facts;  knowledge for insight, and insight into wisdom.

Take Big Data approach by navigating the top-down structure of Goals-Strategy-Execution-Outcomes. The conversation will move beyond Dashboards, Scorecards, data visualization. It will be about merging Big Data and traditional sources of data into a single interactive experience for the business user so they will be able to view and test different outcomes to "their story," and focus on business goals. Get from 'big picture' initiatives down to how individual efforts contribute to corporate goals. Having a good top-down narrative is an important component. If an organization's focus is only at a strategic level, then execution & outcomes are typically more 'painful' than leaders expect. If the focus is solely operational, (data & apps in this case) organizations typically have tactics but lack strategic direction.The logic steps to analyze data and tell a story include:
1). Define the problem or opportunity
2). Problem/opportunity background
3). Options
4). Recommendation - based on facts or data
5). Next steps.

There are now tools that make it easier to tell stories with data. There are storyboards or 'points' built into their latest versions and is a great way of presenting findings, but the real problem will always be to make sense of data from a business perspective. When data analysis excites business executives, it is usually because of the story it is telling them about the underlying real world entity. This entity may be a person but can be any generator of data such as sensors. It is a pattern in a population or across time that is discovered in the data and becomes the story to be told. The challenge and skill are in the mapping of always imperfect data to the always complex underlying reality. The provenance of the information being presented must be readily available and a part of the story; such as using a few interesting and easy to understand graphs to picture the problem at hand and show outliers to define the focus of the story. Then options -- recommendations -- next steps - create a lasting effect of storytelling.

Big Data has to tell a good, not necessary big story to tailor audience. Storytelling is essentially one of the best ways to engage the audience. A data scientist can have all of the tools, technology, and skills - but understanding the business context of the data is equally as important in order to tell Big Data stories. Big Data analysts/scientists can grow into the subject matter expert and thought leaders by telling great Big Data stories or parables.


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