There has never been a better time to be a data analyst. There has never been more data. There has never been more boardroom attention. There has never been more human interest in both our own data and that of other people.
So, for all the talk of geeks inheriting the earth and Harvard Business School proclaiming data science to be the sexiest job on the planet, why is it that little physical evidence exists of brands mastering the application of data analysis to their business practices? Even Tesco, perhaps the brand most synonymous with data analytics, has not been immune to market forces driving down both its market share and stock price over the past five years.
Creating competitive advantage through analytics is difficult both to achieve and sustain. Leadership is a critical issue. The person accountable for analytics within an organization often does not hold a decision-empowered position or is not connected enough to make an impact on business strategy. A second issue is one of adoption. Having the correct strategy and gaining broad-based adoption of it are two separate issues. A third issue is one of transformation. Few companies can communicate the process of how they turn data into information, information into insight, and insight into action.
It starts with consultancy. Before anybody starts adding up all the ones and zeros, or working out how to tag a mobile app with tracking code, SapientNitro works collaboratively with our clients to determine the scope and shape of analytics services that will succeed within their corporate culture and service their business needs. Broadly speaking, there are three target operating models that data analytics functions might form. Each has its strengths and weaknesses, and it is often the culture, not strategy, of the business that dictates which solution is most appropriate.
The centralized model benefits from having only one point of failure, easy knowledge sharing, and the most latitude for capacity planning. However, centralized teams risk creating a bottleneck and making prioritization of projects more political. Conversely, the decentralized model benefits from implanting knowledge at the point of use and giving individual business units full autonomy. However, this model hinders knowledge sharing and risks effort duplication. In the middle stands the hub and spoke model, which is usually the most popular, trading off the positives of the other options while minimizing the negatives.
Having a clear target operating model with a widely communicated purpose is a key driver of success. Yet leaders in the field of analytics go further. They understand their investments in analytics, from direct labor costs and software to licensing and agency fees. They also know what commercial impact this work delivers and can, therefore, set their level of investment intelligently. Without this appreciation, data analytics is doomed to remain a cost center, missing out on the link to revenue growth. This explains why many client analytics departments are fundamentally understaffed, and why companies like us provide analytics services in such volume.
The diversity of types of analytics – from advertising effectiveness to experience optimization to business cases for digital transformation – is significant, as is the breadth of techniques applied to solve a vast array of commercial problems. SapientNitro has developed a unifying framework of analytics that we refer to as “The Analytics Value Chain.”
In this value chain, there are five incremental steps to achieving the highest levels of analytics maturity:
The promise of data analytics is real. And data analytics as a driver of growth is here to stay. Bridging the gap between the promise and value realization is best achieved by a systematic approach. Linking effort to commercial outcome and implementing analytics across the entire value chain provide the best chances for success. But in order to achieve success, you need to set yourself up for it. This means not only ensuring that analytics is plugged into the highest levels of your organization, but also appropriating data-driven decision-making as a company-wide issue and adoption measurement as a key metric of success.
To read more about each of the links in the Analytics Value Chain and see exemplary cases of application from our industry, be sure to download the full article PDF below.