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Banks are under more pressure than ever to deliver products and services that meet evolving customer needs while maintaining profitability. For commercial banks, this means having a clear, data-driven understanding of the full profitability of each corporate relationship.

With big data and analytics becoming such an important strategic initiative for businesses, the margin of error for project planning and execution can’t be taken for granted. There are a lot of critical steps involved to ensure you are getting the most out of your analytics investment. You need to have a dependable plan to establish key business requirements, reaffirm business goals, and match your investment priorities with the overarching business strategy.

Big data, data discovery, and data science.

With the advent of Hadoop, the question of whether it would take over certain data warehousing functions or replace data warehouses altogether has become a hot-button topic of discussion. Finding an answer to this question really depends on how a data warehouse is viewed by individual organizations.

If I were to tell you that companies around the world are prioritizing structured data analytics initiatives, you wouldn’t think twice about it. Given the progressive climate of big data and analytics, it seems like that would be a fair statement to make. Especially as many businesses continue to count on their relational databases, ERPs, CRMs, and other data management systems to organize, structure, store, and define their structured data sets to run more meaningful analytics.

One of the most important lessons we are learning in the Big Data and Analytics Age is that simply having access to innovative technology isn’t enough to improve business outcomes. Technology needs the proper human input and interaction to elicit the types of results business leaders are expecting. Technology shouldn’t be counted on as a magic wand that will always deliver upon request.

IBM Watson has been getting a lot of publicity for helping organizations tackle real-world issues on a national and global scale.

We are entering an age in which corporate predictions and decisions can’t be made without sound analytical backing. Without data credibility, even the most promising ideas and initiatives will be shot down. In an interview with the Harvard Business Review, Tom Davenport (author of the book, Analytics at Work: Smarter Decisions, Better Results) expounded on this point: