Thursday, October 1, 2015

Big Data's Big Insight

Big Data needs to take people-centric approach, leverage Systems Thinking, highlight forward view, not backward view.
We know businesses should collect the 'right' data, cleanse it up, and hunt for data talent who can readily understand, ideally through intelligent visualization, to capture business insight and foresight from it. Businesses must also design integrated, coordinated systems to realize business outcomes from data-derived insights into customer problems that the business can solve. To put simply, Big Data means nothing without systems of insight.



Data tends to be decontextualized in many operational settings. The data also tends to be handled in bulk in massive quantities. Mass consumerism has led to mass delivery systems. The contextualization of data requires sophisticated data objects rather than mere counters. It also requires the depth of consumer awareness and sensitivity. Whether the prevailing bulk mentality can transition into insights satisfying the needs of individuals remains to be seen. But it is part of the equation relates to the sophistication of data system in use. It’s not just about the amount of data or processing power, but true sophistication in a structural sense.


Companies will need to construct internal channels that tailor customized services for their customers as informed by Big Data. Big Data can make suggestions, but it cannot do "the plan of action." There could be a "feedback loop" potentially involved here, that could provide the tailored customized service based on the result of the customer interaction, as well as to possibly influence the Big Data Analysis algorithms employed by the company. Having a feedback loop for customized service is a great suggestion. So there's more information than a general increase in sales that you can't precisely attribute to any particular promotion or cause or set of customers. Some consumer driven data based businesses may now be able to tell nearly exactly which of their customers are buying in response to targeted promotions based on loyalty cards. And that is where online marketing has the information edge on brick-and-mortar stores, they can see more easily what their customers are buying and at what click point in the process and they have already collected more information on them than a physical shop unless the physical shop has issued a loyalty card to the customer.


The ultimate goal for any data analysis is to make effective decisions. Data regardless how big or complex does not make sense if not converted into usable information for decision making to identify, analyze and solve issues for the better of the company and its stakeholders including customers. This needs the right tools and knowledge. It brings together insights for teams that unify developers, data and business experts to work directly with a digital leader, with a budget and an outcome of managing. There are many different types of data and insights. For example, industry and market data may show clear general trends, these must then be followed up in a designed cross-functional process to effectively convert these insights or foresight into persuasive business cases for change. Big Data is like the raw ore that data scientists work to produce something of value. Data scientists convert raw big data into useable products and actionable insights which are the true value of any data. Using the analogy, data scientists will also at the outset specify what data needs to be collected - the right compound of ore/alloy and the right processes to produce the desired product - ultimately, an insight that can be successfully applied to create product/service that customers want to buy or that change makers can act upon.


It's essential to make Big Data valuable, able to serve a purpose. Big Data needs to take people-centric approach, leverage Systems Thinking, highlight forward view, not backward view, indeed, Big Data means nothing without Systems of Insight.









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