5 Ways to Convert Data into Business Value
A new era of mainstream adoption continues to open up for organizations across the globe. Big data and advanced analytics technology offer the promise of unprecedented insight into business operations and our customers, enabling us to not only improve operational efficiency, levels of service, revenue and business models, but also boosting customer-centricity.
Various sources of information are providing companies with access to unprecedented amounts of data, whether they come from management systems, customer data, customer feedback, market data, or social media. However, a core challenge is finding how to convert, use and maximize the value of data collected into business value. Gartner estimated that it would take 5-10 years to reach the ‘plateau of productivity’ in the Big Data space. Here are 5 tips to remember when driving your Big Data initiative...
Lesson #1: Start with What you Already Have
By preparing existing data stores for analysis through integration, tagging, and other methods, the overwhelming task of collecting new data should take a back seat to working with existing datasets initially.
Tools and techniques are being adopted ahead of learned expertise. Organizations will need a well-considered strategy for integrating big data into their information architecture so that it becomes a core part of the platform and the way they do business.
Lesson #2: Involve End-Users
Involve end-users throughout the program and not just during requirements gathering and deployment phases. It's a common practice to involve end-users actively during requirements gathering and acceptance testing phases. However, the users need to be actively involved in other phases as well, namely prototype design, solution development (application walk-through) and test planning phases.
Change control is an inevitable aspect of any large program. The end-users need to be actively engaged as part of change control process. Active involvement from end-users comes at a cost. If you try to save this cost, it may have a detrimental effect on your program.
Lesson #3: Keep it Secure
The temptation to source as much information as possible can be overwhelming - informed choices give marketing much more certainty and monetization potential - but remember to maintain a "fair usage" approach. Due to a number of data breaches and skepticism over publicly used datasets being truly anonymized and aggregated, new data protection rules and legislations have and are still coming into force. Legal pitfalls, issues of privacy, data protection, and security are heavy, so be sure data analysis and usage is legitimate, and that it's securely encrypted and curated.
Lesson #4: Don't Underestimate the Data Integration Challenges
Deriving value from big data usually is dependent on processing unstructured information - video feeds from shop floors, telematics sensors in vehicles, GPS sensors in mobile devices, speech to text files and a host of other bits and pieces of information that are not readily processed. Most organizations do not have experience cleaning these types of data.
Lesson #5: People Power
When planning on implementing big data initiatives to improve performance and drive growth, its imperative to have the right people to go alongside the right tools and metrics. The right data scientists and engineers working on the IT functions of the business are of course crucial to data initiatives, but we’re businesses really driving forwards today are appointing more C-Suite level professionals, like Chief Data Officers, to not only attain buy-in from management but importantly across the organization.