Big data can translate into big money, both for the companies looking to profit from big data analytics and for the solution providers providing big data services. However, for big data to yield a higher ROI, big data projects need to be well-defined and make the most of available information sources. Big data analysis can tell an organization a lot about its customer base, marketing programs, product development and manufacturing, and new areas of profit. It’s the big data consultant’s job to bring the right skills and experience to the project to make sure the analytics yield the right insights.
There are many reasons that big data initiatives fail. According to a report from PwC, 75 percent of companies surveyed believe that they are getting the most from their information assets, but the truth is that only four percent are using data to maximum advantage. The PwC survey shows that 43 percent of companies get “little tangible benefit” from their data, and 23 percent get “no benefit” from their data.
Clearly these companies could use big data consulting to help them unlock insights from stored data. Those few companies that are getting the most from stored data are proof that big data projects pay off. It’s up to big data consultants to show their customers how to realize the same returns.
Becoming a Big Data Consultant
Any solution provider familiar with data warehousing and business intelligence has what it takes to become a big data consultant. Big data applies the same data-mining principles, but on a larger scale. In many ways, the big data arena is still being defined as new use cases and reference architectures emerge, so now is the time to extend your expertise.
Being a big data consultant requires business savvy as well as technical expertise, because you will be working with personnel in all functional areas. Expertise in Hadoop and NoSQL will help, as will big data certification, but solution providers don’t need a background in big data to develop a big data consulting practice. Start with a data management practice and database development. Analytics applied to data warehousing provides a solid foundation for moving into big data.
Most IT departments are more than willing to leverage company data, but they don’t know where all the data assets are stored or how to properly mine them. Big data consultants can step in, not only to inventory available data assets, but also to develop a strategic plan on how to apply the data to reveal the insights that management needs.
To deliver big data ROI, it’s best to start with what you know. Current customers are good prospects for big data consulting services. Not only are they comfortable with you as a service provider, but you also have an understanding of their business challenges and are in a better position to develop effective use cases.
Selling Big Data Services that Yield ROI
When approaching big data consulting prospects, you have to show customers exactly where they will see ROI from big data insights. Start with the basics.
Consider how big decisions will have a profound impact on future operations. However, many big decisions are usually opportunistic rather than strategic. It has been proven that data-driven decision-making is three times more likely to yield desired results, yet 62 percent of executives surveyed by PwC still prefer to “go with their gut.” At the same time, 59 percent of executives estimate their next big decision could be valued at $100 million or more, and 16 percent said the right big decision could be worth $1 billion.
If companies are three times more likely to benefit from data-driven decisions, then using big data for better insight seems essential. This is a great proving ground for big data.
Another factor to consider is competition. Organizations that fail to adopt big data run the risk of being outmaneuvered by their competitors. Even when they have the data, 58 percent of executives indicate that moving from data to insight is extremely challenging.
To help customers maximize big data ROI, big data consultants need to work with customers to apply a four-step assessment and execution model:
Step 1: Discovery – Identify the problem that needs to be solved—the use case—and assemble internal and external data sets that could unlock insight. Data sets could cover a wide range of areas, including customers, products and demographics. Then develop a data lake architecture as a foundation for analysis.
Step 2: Insights – Catalog the types of analytics techniques required (e.g., forecasting, regression, machine learning). Map the tools to the data sets and start developing a prototyping environment with analytics and visualization tools. Develop data discovery processes and a provisioning model to access the data.
Step 3: Actions – Develop a decision model that turns insights into actions. Integrate decisions with business processes and embed decision results as appropriate. For example, embedding big data insights into the design of new products or services could result in substantial additional revenue.
Step 4: Outcomes – Refine the integration between insights and actions and monitor outcomes, filtering out variables such as dynamic market trends. Also, test the model regularly and observe and refine the findings.
Applying this type of operational model shows cause and effect and how to turn big data insights into revenue. Outcomes are measurable and can be traced directly back to data discovery so that the ROI from big data is proven. As an outside consultant, it’s easier for you to extrapolate this process, helping senior management identify and articulate its business goals and developing the big data infrastructure and processes to deliver value. The ROI is clearly there, and once you get a pilot program under way, you can prove your value and the ROI that you deliver as a big data consultant.