Making a success of big data analytics is a bit like constructing a skyscraper. Foundations need to be laid and the land prepared for construction, or else the building will rest on shaky ground.
The success of any analytics project depends on the quality and relevance of the data it’s built upon. The issue today is that companies collect an exponentially large volume and variety of information in many different formats and are struggling to convert it all into useable insight. In short, they're having trouble preparing their big data and unlocking the value.
For instance, before analysis, a business may need to aggregate data from diverse sources, remove, or complete empty data fields, de-duplicate data, or transform data into a consistent format.
These tasks have traditionally relied on the expertise of the IT department – even as ownership of analytics projects has shifted towards line of business leaders. But as volumes of data grow, preparing data in these ways becomes more laborious. With this mounting demand, IT teams can take weeks to fulfill requests.
Businesses have recognized this and are investing in data preparation technologies. Two thirds say they have implemented a data preparation or wrangling solution to manage a growing volume of data, and 56% have done so to help them work with multiple data sources, according to research from Forrester.
Today’s data preparation tools aren’t restricted to those with IT expertise and they allow companies to spread their analytics processes to individual lines of business. Not only does this dislodge their data bottleneck, but analyses are managed by subject matter experts with a keen eye for the most valuable insights.
As organizations are overwhelmed by the flood of data, it’s also important to unify data from the various sources and ensure they are accessible and consistent across the business. For example, CaixaBank is storing vast pools of data on one consolidated platform – commonly referred to as a data lake – so each of its business units can access, analyze, and digest relevant data as needed.
From here, businesses can start experimenting with the data to explore new ideas. For instance, Telefonica worked with a single view of its data to test a new algorithm designed to create personalized TV-content optimized pricing models for customers. After successful testing, Telefonica made the algorithm live and has since seen higher TV viewing rates and improved customer satisfaction, while also reducing customer churn by 20%.
In addition to unlocking the commercial value of data, there is a strong regulatory driver for companies to gain more control and oversight of their data. When the EU’s GDPR comes into effect this month, companies will face harsh penalties if they are not transparent about the way they collect, use, and share customer information.
To reach skyscraper heights and build the businesses of tomorrow, data preparation must rise up the corporate agenda and be a priority for all companies looking to unlock the value of their ever-increasing volumes of data.
From data scientists and analysts, who work closely with company data each day, to business leaders exploring new ways to improve the way they work, Oracle has a set of rich integrated solutions for everybody in your organization.