Big data. Data science. Data analytics. When most people think of these terms, they think of massive IT projects – those that Coca-Cola might initiate, for example, to measure the effectiveness of a new marketing campaign or the receptivity of a new product. Big data is also used by large corporations to predict future customer behavior based on past and current.
In short, data science has been seen as something only for the “big boys” for two primary reasons:
- A big data analyst (scientist) comes with a hefty price tag, and having one or more in-house is not financially feasible for small and mid-size businesses.
- There is some generalized belief among smaller businesses that they don’t need data science because they are too small and can gather the small amount of data they need through Google Analytics.
And these reasons have resulted in 77% of smaller businesses not having a big data strategy. It’s a big mistake. The use of data analysis solutions means that a company can make informed and much wiser decisions about products, services, marketing, and more. It can give a business a competitive edge and seriously impact the bottom line.
Even the Big Boys are Dragging Their Feet
So that smaller businesses will not feel incompetent, it should also be noted that Forbes and Ernst and Young recently conducted a survey of executives from large international enterprises and discovered that 70% of them state their big data analytics strategies are still in the early stages of development. But they all also stated that they know this must be a key initiative in their immediate futures.
It’s really no longer a question of “if” a business should incorporate big data and BI into its operations. It becomes a question of “how” to do it. There are two primary ways of how your company can incorporate big data:
The business owner may be “low-tech,” but a number of vendors have been working on software tools specifically designed for non-IT business executives.
The problem to date with these solutions is that they can be complicated and not user-friendly. The challenge going forward is to make the presentations of data simple and clear so that non-techies could understand and use them. Someone still has to interpret the results, and until that interpretation is better designed, there are stumbling blocks.
Still, a self-service solution is one of the coming trends, and it will certainly be less expensive than an in-house data scientist.
2. Outsourcing Big Data Analytics
There is a growing field of big data analytics companies that offer data science as a service to companies without the internal wherewithal. On the surface, this seems to be a great solution – it is budget-friendly, and it will involve not just data collection and “crunching,” but interpretation and recommendations as well, if the right big data consulting firm is selected.
But this option does not come without its drawbacks as well. The data analytics outsourcing market is growing and there is an opportunity to get great results. But first, be certain that you understand the advantages and benefits weighed against the drawbacks. Here’s a look at the pros and cons.
Advantages of Outsourcing Big Data Analytics
- The obvious big advantage is cost. Outsourcing data analysis means that, even if you could find a data scientist, you could probably not afford him/her. And, of course, as is with all outsourcing, not having a full-time employee also means savings on benefits, payroll taxes, etc.
- Getting expertise from a big data consulting firm will result in not just the right data mining, crunching, and analysis, but it will usually be accomplished far faster than a business could do so with self-service solutions. They have all of the big data technology at their fingertips, as well as the experience.
- Data science and analytics outsourcing allow the management and executive team to focus on other core operations of their business. They can have the analytics presented to them, nicely packaged, and can use those results to make sound decisions. Self-service solutions are time-consuming and a struggle.
Looking at the disadvantages, one thing to keep in mind is this: while these drawbacks are real, they can be mitigated if the business understands the key do’s and don’ts when contracting with analytics outsourcing companies:
- There is always a risk of exposing sensitive company data and losing confidentiality. Doing the right research and ensuring that data security is clearly provided for are key steps. Measures to ensure security should be contained in any contract/agreement that is crafted.
- Legal and other issues can arise if the contract is not “tight.” If, however, you get the right legal advice on your end, these issues can be prevented up front.
- A consulting firm may be building its reputation and making that the key goal for its business right now. Thus, it may take on more projects than it should. This can lead to delays and errors. Be certain that the up-front agreement has timelines and deadlines and that there is a team dedicated to your project. Plan regular meetings with that team or at least the project manager.
- There can be communication breakdowns if the business owner sits back and relies on the outsourcer to handle it all. Again, this is mitigated if there is a commitment on the part of the business owner to be a part of the consulting team.
When the Advantages Win Out
In most cases, the advantages of outsourcing big data analytics outweigh the drawbacks, especially for mid-sized companies. Executives are not data scientists, and most do not want to be. But, the use of data and BI to inform decision-making just makes sense today, if a business is to remain competitive.
There are many things to think about before making a decision on a specific consultant firm for this operation.
- Due diligence in research means that the firm is thoroughly investigated, has a solid reputation, and has been in data analytics for some time. Reputable firms will provide plenty of references.
- Make certain that the firm has experience in your sector/niche.
- Much of the success of outsourcing data analytics comes from the relationship between the company and the consulting firm. It must be carefully constructed from the beginning.
- Be certain, and get it in writing, that there will be a dedicated team for your project(s) and that the results will be far more than just data aggregation. Reports must be presented in clear understandable terms that will truly inform your decision-making.
The time is past for small and mid-sized businesses to think of big data as a function of “big boys” only. There is much to learn about what you are doing well, what can be improved, and what your long-term business decisions should be. If you can develop a good BI strategy based upon key metrics for both your company and the sector as a whole, you gain the competitive advantage and will have the wherewithal to grow and prosper.