A growing number of businesses are investing in and investigating a big data strategy, as the practical benefits become clearer through real world examples, and vendors simplify their solutions.
The first key issue that a business needs to understand is that big data isn't about a specific volume of information. Big data relates to any constant stream or silo of information that will build up over time. Managing that type of information is better done with a big data solution than classic database management techniques.
What's the benefit of big data?
Having established that you possess or will inherit big data streams, the second factor is establishing how you will extract value from that data. There is no point in running big data projects without material benefit to the company. These can include using travel information to make routes more efficient, analysing sales information to streamline pricing, spotting fraud patterns among transactions and so on.
Once that goal is established, you can create a road map, likely starting with a proof of concept on a small scale. Vendors love to wave white papers and grand-sounding statistics at IT buyers, but you will need to establish how well a product will work for you before committing to an investment. This can be done through an open source test, demo or trial.
Understanding big data within the business
Whatever the company, big data will ring alarm bells among some managers. They may fear losing control of an asset, worry about redundancies or have other reasons to fear a project. Explain in clear terms what the big data project aims to achieve, how it benefits the company and the people who will work on it.
For larger companies, having cross-management buy-in is essential for the success of any project, so keep all stakeholders regularly updated on progress. Highlight any flaws, issues, or changes and how they can be overcome as they arise, rather than burying bad news. Big data projects can be complicated and have a steep learning curve, so be up front about the challenges.
First results and future plans
Once a big data test has been completed, allow everyone to view the post-test results and analysis. Use clear language to highlight the positive and negative outcomes, and the overall benefit. Even with a failure, the company may be keen to try another test in a different way. Through success, the company may want to broaden its big data ambitions and look for new ways to work with their data. Remember to go back to step one for each project and do not assume big data will work in all cases.
Types of data will affect your big data plans
Different types of data, such as unstructured, semi-structured or rigidly structured require different types of analysis, the frequency of data from real time (location) to regular batches (POS) to legacy files will change what outcomes can be achieved, and the type of analysis from batch processing, real-time or other types may affect your choice of big data solution.
Treat big data as a great possibility, but do not assume it will end up with a win in every scenario for the business, but learn from every project to improve the chances of the next one. Contact CloudScope today for more information.