Saturday, September 13, 2014

The Big Focus of Big Data

Big Data needs to be value-driven.

There’s nothing new about data being important to business. The only difference now is there’s more of it and the processes have changed quite dramatically. But it’s still just data. Fundamentally, what are the big focus of Big Data and analysts nowadays?

Focused on the human aspects of cultural change---what questions do you ask? How does the data need to be displayed to be useful? As with many technology trends it seems to start with the technology-enabled business capability followed by business leaders asking big strategic and operational questions. You have to understand the fundamental issues the customers/verticals are having as it relates to things like utilization of multi-timescale data/information elements within the context of core business driven workflows. Drive out the inefficiencies, enable them to spend more time on value add activities.

Big Data needs to be value-driven. Data-driven organizations are only a means and not a strategy. The need to have accountable "value" and expectations from the data seems to be a hangover discussion for many after they take Big Data initiatives that are technology-driven. Big data has enormous potential over the long term, but there also needs to be a realistic assessment of what is possible given the limitations of the tools companies are using today.

Big Data tools need to be effective. Big Data adds new elements. Now Data Analysts are trying to add social media data feeds in order to predict demand and behavior. Social media datasets are typically associative, not relational. They don't conform to the traditional DW model, there's a lot more data, and you have to mash-up structured & unstructured data. So the reality is that businesses go beyond just underestimating what is required in terms of processes and analysts. In many cases the capabilities of the current tools tend to be overestimated as well. The popular big data platform meets the basic requirements of many SAAS applications and unrealistic expectations lead to failed projects.

Don’t try to boil the ocean all at once. Big Data does not have to mean Big Results all at once. Prime the pump - repeat and rinse. Iterate. Learn. Grow. Every use-case is different. Every value point may be different. There is a huge gap amongst the Big Data 'platforms' / 'solutions' / 'services' and the business leaders understanding of those to decide on which one to go with and finally the actual users who are using those solutions to make their day-to-day decisions and drive business.

Pay more attention to people and process aspect as well. Many organizations have underestimated what it takes to enable organizations to leverage Big Data platform. Besides technology, you need to look at the people and process aspects. A key success factor for Big Data projects are Data scientists who understand the business, know the value questions to ask and their relative priority. An iterative process for Data discovery enables maturation of business requirements and allows for early wins that help with leadership buy-in/support. This requires a shift in thinking from waterfall to more agile methodologies.

Continually improve to overcome learning curve. Most of organizations are only at the beginning of the value curve in terms of business transformation enabled by Big Data & Analytics. The analytics only relate to IT/technology departments ability to harness and manage the data at scale and cost. This next cycle will be championed by cross-functional leaders that recognize the value that is achievable, and capture it, by doing things differently. (1) Servicing customers/subscribers on a proactive basis (2) Marketing to the individual based on habits, likes, trends, location vs. current generally accepted practices of marketing to the masses. (3) Developing new business models, product and service creation.

Big Data & Predictive Analytics are here to stay – it is transformational. Predictive Analytics have great business value; but like any new technology, IT and business users are struggling with the goals and objectives. Hence, Big Data has to have Big Focus: What results are meaningful to the organization, and how to frame the big questions about Big Data, in order to solve big business problems, not just overcome technical challenges.  


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