Exploiting business operations data remains a top priority for current and future success of technology companies in the cognitive age. Technology companies are not immune to the challenges related to building effective data warehouses that plague other companies in other industries. While the tools – cognitive and analytics – are certainly new, data warehousing has been around for generations. However, the advent of cognitive technologies has raised the competitive necessity of getting data right for technology companies, and all others.
Moving quickly with innovative, new products is part of the competition game. Yet, the byproduct of exponential growth of new products has been an explosion in the volume, pace and type of data, proliferated by social, mobile, IoT, and related platforms. New products generate a tremendous volume of data that needs to be interpreted almost in real time to stay competitive in a global market. Technology companies are at the epicenter of this quick movement and face the challenge of building effective data warehouses. Often, they find themselves in search of the holy grail of an effective, cohesive data strategy.
“The barriers to success have not changed for technology companies. Yet, given the sheer volume of analytics and the pace of competition, the bar has been raised. The opportunities created by getting data right and the costs of getting it wrong have amplified today.”
-- Steven Perkins, National Managing Director
Grant Thornton, Technology Industry Practice
For example, some tech companies act as an intermediary between consumers and businesses. Such companies use data harnessing and interpretation to predict consumer behavior for other businesses that are their clients (e.g. example advertising-driven tech platforms, or other businesses predicting consumer spending). Such companies should almost simultaneously acquire the data and interpret it in order to make predictions.
Mining the available data through the core business and working towards a real time interpretation of that data is both a challenge and an opportunity. The ability to identify buying patterns, spot opportunities and areas of concern, and communicate this in real time is vital. Such middle-man companies need to harness the power of data for both sales effectiveness and an operational effectiveness.
In addition and of note for companies that are focused on B2B is that their success also depends on their ability to lead by example. That means that they themselves need to use data to improve their own operational effectiveness before selling to their clients data tools and interpretations that they can use to make business decisions.
Utilizing global labor markets to source talent for data analytics
Data scientists, analysts and architects are highly prized and highly priced. Competition to snag the best people on the market is acerbic and talent that has data science or data analytics experience is hard to find. In these circumstances, competitive salary offers are absolutely necessary to entice the right people to join your team. But even with this basis in place, the struggle remains real. Technology companies do have the luxury of a deep pool of technical talent, some of which can be redirected to help with the data challenge. This raises an internal talent challenge, as you are often borrowing talent from product development to staff your data needs.
Yet, technology companies also have another possible solution, which is to use the global reach that most of them have and source talent globally. Emerging markets such as South America, Eastern Europe, or Asia have solid university systems that produce a highly-skilled technical workforce that can constitute a very advantageous labor pool. Of course, since most technology companies are global, competition in this labor pool still remains tight.
Getting your tech people to talk to your business people
While hiring the right talent can make the success of a company, establishing the right channel of communication between your IT and business sides can have a similar effect. Leveraging the power of data successfully across the company depends on aligning your people resources first.
Gathering and mining data successfully is not enough without a seamless integration between technical and business capabilities and goals. Data-related projects need to be a joint effort between the technology teams and the business users from the very beginning. Both sides need to be open-minded and contribute to that initial structure.
Possibly the hardest to find, but a most successful solution for companies, is to have a crossover resource, a person in charge with a strong interest in data and a background in finance or accounting.
“Success depends on running a centralized effort led by business operations and finance. People in this group should have the aptitude and drive to learn new skills, while having a solid background in operations and finance.”
-- Roy Nicholson, Principal
Grant Thornton, Business and Technology Advisory
Tips to make data opportunities transcend siloed groups
The most common challenges tech companies identify are: 1) having different groups in the company that gather their own separate data using different tools. (This is often a byproduct of rapid growth and an effective M&A strategy.) 2) having decentralized reporting.
What if pockets of your company are already in the process of developing their own kind of warehouse data strategy? How do you bring all these pockets into a holistic company strategy?
Sometimes data grows organically as the company grows. But there comes a point when the company needs to reconcile all these different sources of data and the effort to do so can become taxing. Early introduction of a data governance model can provide the scaffolding on which to organize data use. This will save you substantial rework later.
Another aspect of the same problem might be related to different products that the company launched in time, which are still being used and generating data. A great example are video games? Products that were launched anywhere from five to ten years ago and that still bring in telemetric data? How can you consolidate this kind of data in a new warehouse?
Technically, consolidating disparate data is not difficult, but rationalizing disparate data is. What is a must-have is a data governance framework and model, as well as buy-in from your leadership to achieve a top-down sponsorship and cross-functional accountability.
While the challenge for tech companies and other companies alike remains justifying the expense of introducing data governance early in the life of data, tech companies pay a higher price because of the accelerated pace at which their industry scales. In addition, looking at the process of digitization from end to end is a precursor to a great data strategy.
Tech companies are moving the fastest towards a fully cognitive age and, for that reason, they are also experiencing upfront challenges that always accompany change. New technologies bring with them new waves of data, which makes a top priority the necessity that companies come up with solutions to build a holistic data strategy across the enterprise. This includes standardizing data governance, centralizing the associated data platforms, and investing in the new skills and training required by this change. These are measures that can be adopted by companies in all industries, but tech companies in particular need to be quick on their feet.
Steven R. Perkins
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National Managing Director
Technology Industry Practice
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