You probably don’t need to know that yet another report has been published about the benefits of big data. Tech giants like Google, Facebook and eBay are in on it, using a mix of bespoke, freeware and licensed technologies to monetize internal data assets by combining it with freely available big data. Even Dilbert has something to say about it!
But how are organizations implementing solutions that would enable them to tackle the magnitude and unleash the potential of this big data opportunity?
Long Road Ahead
Typically, scenarios involve senior executives sanctioning the use of a large source of funding to initiate the creation of a big data platform expeditiously. Organizations soon realise that they need to deploy analytics to make sense of this data.
Some organizations embark on an “agile” program. It might have a platform that includes Hadoop of some flavor for distributed storage. And some data framework to handle machine learning and real-time streaming, such as Apache Spark, and many other disparate moving parts.
The result? After a year or two and a few million dollars, a workable big data platform is unveiled.
Unfortunately, this is often too little, too late. Why? The organization has lost critical time and resources. They have handed the advantage to competitors who took a different tack.
Run With Big Data Analytics
Those who have been successful have pursued a very different strategy and approach – one that allows the infrastructure follow the needs of successful pilot projects. Crucially, this approach ensures that the big data platform is funded by the analytics it enables.
Now, how does this work in practice? Really pretty much in the same way as for operational analytics, except that we will be incorporating big data alongside operational data!
The Four-Step Approach
- Identify a few pilot projects that have a strong business case and require external sources of big data. For instance, you might want to see whether you can leverage any insights from the tweets about your organization. You could pilot a project using sentiment analysis to uncover the topics and positive or negative feelings that your customers have towards your business.
- Prioritise these projects in terms of value to business and ease of implementation. Initial successes will act as proof-points, enabling you to build out the skillsets and resources within your organization to be able to tackle bigger and more difficult analytics.
- Evaluate big data technologies on a short sharp engagement. This testing can be done internally if the expertise exists or with external consultants focusing on the analytics projects most likely to succeed and with high business value.
- Continue the identify, prioritise and test process over a few cycles. This gives you time to understand what your organization’s big data needs are, and provides valuable input for the eventual delivery of a ‘fit for purpose’ big data technology platform.
Need More Convincing?
Surprisingly, this evolutionary approach takes no more time than the approach of first spending two years to deploy a big data analytics platform before using it for business benefit. Better than that, at no point during the journey is the organization ignoring its operational analytic needs.
There is even an added advantage. This gives an organization time to embed and integrate ‘big data thinking’ in the organization. This is something that happens incrementally – you can’t expect an organization’s ability to analyze data and use those insights from novice to experienced in one fell swoop. This is organically achieved in the evolutionary approach.
Clearly, this is a much better way to move into big data!!
This post first appeared on Forbes TeradataVoice on 06/05/2016.
The post What You Need To Do To Get Big Data To Work For You appeared first on International Blog.