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by laying out a strong road map to success and following through, we’ve managed to make big data a huge asset for both our clients and ourselves
Since 1970, Environics has been a leader in the field of market research consulting. With an original focus on public opinion polling and public affairs research, we’ve grown into a medium-sized consultancy providing services in a range of fields, including financial services, health care and pharmaceuticals, consumer packaged goods and telecommunications. Until recently, we’ve relied heavily on the collection of survey data to understand consumers and form insights, but over the past five years, there’s been a clear shift.
Big businesses around the globe are now looking for more data-driven decision-making techniques and, in many ways, survey research alone just won’t cut it anymore. Unfortunately, for many small- and medium sized organizations like us, this shift poses a major challenge, as true data-driven decision-making usually means making a big business-sized investment.
It’s a barrier that could have represented a huge setback for Environics, but by laying out a strong road map to success and following through, we’ve managed to make big data a huge asset for both our clients and ourselves. We’ve shared that road map below and, by building on our example, your business can start taking advantage of the insights big data has to offer too.
The Four Key Checkpoints:
Our road map was laid out with four key checkpoints along the way, each of which is pivotal to achieving big data integration. That being said, what will truly impact your success is an understanding that the implementation of this road map will involve every aspect of your business and a change in the overall mindset of your organization. With that in mind, let’s breakdown those checkpoints.
Checkpoint 1: Vision and Leadership
As we’ve already mentioned, the implementation of big data analytics impacts every aspect of your business, so it’s extremely important that your leadership team defines what they are looking for in terms of success. That means creating clear short-term goals that contribute to a long-term vision. Try to avoid simple long-term goals, as they can quickly lead to open-ended development. This type of work tends to become more expensive in the long run and makes the return on your investment difficult to calculate.
Here at Environics, we created a Digital Innovation and Data Management team that’s committed to the development of new data management and analysis tools. But, rather than simply investing in expensive software and staff, we’ve focused on exploration by building relationships and partnerships across the data analysis industry. We’ve even taken it a step further, looking beyond our own industry’s expertise to help us form the most effective development strategies possible.
By borrowing from the software development industry, we’ve utilized capability maturity models (pictured below) to keep our development goals on track and moving forward. As you can see, the model allows us to place each of our development projects into a category of maturity. As our projects continue to develop, they are defined and managed based on their current state of maturity. It’s a relatively simple model, but experts in the field of development have proven its effectiveness by implementing it across the software industry.
Checkpoint 2: Analytical Skills
According to a recent study from the McKinsey Global Institute, in as little as five years, business professionals could be facing a 50% to 60% gap in the demand for professionals with deep analytical skills. In other words, there’s a shortage of analytical talent coming, and you’ll need a solid approach to recruitment if you want to keep up. Unfortunately, traditional recruitment techniques like postings on job boards simply don’t allow for a broad enough range of skills to be captured – and competing for recruitment with big business is far too expensive. Instead, we recommend relying more heavily on partnerships and establishing roots in analytics communities.
Although Environics is a relatively medium-sized business, as a data consultancy, we’re admittedly a bit ahead of the curve in terms of analytical skill. We employ teams of analytics experts from a variety of backgrounds, but at the end of the day, it’s the partnerships we’ve forged and our own forward-thinking that have been key to our development of talent. Our partnerships with Georgian College and Humber College, for example, have given us steady access to a deep pool of cost-effective, entry-level talent to draw from. Graduates come to us with the basic analytical skills they need to get started – and, with the right vision and guidance, we’re able to develop them into the talent our future needs require.
To use an analogy from the basketball world, if you aren’t the Miami Heat, you probably won’t be able to recruit the Lebron James’s of the world. They can make offers to top-level talent that you, as a smaller organization, simply can’t match. But, if you have the right foresight to know what your talent needs will be in the future, you can be like the San Antonio Spurs and attract high potential talent, mold them into what you need and win it all without spending a fortune on recruitment.
Checkpoint 3: Tools
When it comes to working with big data, acquiring the tools is likely the most daunting challenge for most small businesses. It’s almost impossible to make a large enough investment into this area without being a large organization. In particular, setting up the appropriate infrastructure to host and work with your data can be very difficult. Here at Environics, we’ve found that the best way to deal with this is to utilize the tools that you do have while taking advantage of the various cloud solutions that exist, such as Google Big Query.
By using cloud tools, we can host a variety of databases and scale server performance to our needs at the time. This allows us to connect to, and load, especially large datasets into databases using the analytics tools we already have without worrying about our infrastructure, and whether it’s robust enough to handle our future needs.
Crowd-sourcing communities like Crowdanalytix.com and Kaggle.com can also work well in terms of connecting you with data scientists who can offer potential solutions to your current tool’s limitations.
Checkpoint 4: Tolerance for Failure
Just like any investment, the implementation of big data analytics comes with more than a few unknowns and risks. It’s important to remember that this process involves aspects of research and development, and that not all analytic techniques will lead to tangible benefits. To help illustrate the point, look to Thomas C. Redman and Bill Sweeney’s analogy in the Harvard Business Review:
Companies that aim to score big over the long term with big data must do two very different things well. They must find interesting, novel, and useful insights about the real world in the data. And they must turn those insights into products and services, and deliver those products and services at a profit.
While the two goals are mutually reinforcing, companies actually require two distinct departments. To succeed at the first, companies should set up and manage a “data laboratory” … for the second; companies should set up and manage a “data factory.”
It’s an important analogy to keep in mind because it underscores the fact that some of your of analytics activity will never leave the research floor. Your organization will need a certain tolerance for that eventuality, along with the ability to learn and adapt from those less successful endeavours, if you want to be successful.
Again, as a data consultancy, Environics is already structured to deal with the distinction between developmental goals (the data laboratory) and operational goals (the data factory), with separate yet cross-functional teams working hand in hand.
Although dealing with failed developments can be difficult at times, with clearly outlined objectives and paths that lead from development to operational goals, your team leaders should still be able to effectively evaluate your results.
So, there you have it. A road map to success with four key checkpoints along the way: leadership, analytical skill, the right tools and a tolerance for failure. There’s no question that the shift towards the implementation of big data analysis can put the little guys out there at a disadvantage but, with our road map in place and a bit of innovation, we at Environics have found that big data can work for small business too.
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