Big data is no longer a game changer across most industries, it is simply the norm; we live in a big data world. What’s ironic is that big data initiatives that once started with fanfare and grand expectations haven’t lived up to the hype. Nevertheless, it has helped a lot of industries evolve the way they do business and great focus is being applied to shore up areas of weakness in big data, such as data quality. Once relegated to the back of the line in terms of priorities, that’s now changing…and fast. In healthcare, for instance, big data is being used to improve patient care by drawing on massive amounts of patient data and correlating that data to patients in similar circumstances to improve individual health outcomes and ultimately lowering long term costs to the provider. This type of analysis requires big data, but we still need to work out a few kinks to improve the confidence in the analysis results.
Let’s take a look at how it’s revolutionizing three other industries. The insurance industry is using big data to create self-tailoring, individual insurance policies based specifically on a single customer and not forcing customers into pre-packaged groups. In the media and communications industry, big data is being used to create a seamless customer experience, by combining third-party data with massive amounts of customer data to help companies make the right offer, to the right customer at the right time. And in banking, big data is being used to create a full omnichannel experience across all touch points. This means customers who now access their bank account from anywhere at any time on any device also present a new big data opportunity to collect and analyze both customer behavior and more accurately predict and curtail fraudulent activity before it costs the bank millions in lost revenue. It also means that when a customer makes a deposit, in person, at their financial institution, it will reflect on their mobile app immediately and when they make a withdrawal at an ATM it should reflect on their online portal right away. Big data is a catalyst for change to do new things and solve customer issues that weren’t possible just a few years ago.
These are just a few examples of how big data is being used in a big data world. Virtually every industry is using big data in a variety of ways. However, what isn’t mentioned often enough is how big data quality is either the foundation of a successful big data initiative or a latent liability.
In any industry, data spans not one or two systems, but a series of systems, some internal and some third party. These systems comprise an end-to-end business process, important for any big data initiative, and unless the right controls are in place, errors are bound to occur at any point in time. These errors can obliterate data quality in an instant, jeopardizing any big data initiative and losing the trust of those who consume the data.
Businesses need to stay diligent and consistent with their data quality management efforts to chart a path forward that leads to eradicating potentially egregious data errors. What is required is a standardized, auditable, and automated end-to-end data quality framework to remain vigilant and catch errors before they impact a big data initiative. Data errors are always going to happen, but businesses that institute data quality as a culture, based on principles that are implemented in technology and process, will pull ahead of the competition.
To properly implement a standardized, auditable, and automated end-to-end data quality framework and properly manage big data quality, businesses will need a platform build designed to handle not one, but rather multiple steps from data acquisition and preparation, to data analysis, operationalization, and even visualization. The platform should enable users to source data from multiple data platforms and applications, including vendor products, external databases, Teradata warehouses and data lakes and bring into a single solution for business users to easily use.
The platform should enable users to create automated notifications, manage exception workflows, and develop automated data processing that can integrate the results of the analysis back into operational applications and business processes. By ingesting, processing, and supplying in a single tool, it provides flexibility and convenience to business users who need the “one-stop-shopping” experience of their data quality.
A new tool at your disposal is the combination of two disciplines – data quality and analytics. By applying analytics through the use of machine learning and predictive analytics, data quality results exponentially improve. Analytics can drive stronger data quality rules and stronger data quality rules drive more reliable analytics. This dynamic combination is changing how big data quality is performed to do things never thought possible in traditional data quality circles.
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