According to a recent survey, it looks like those of us in the analytics community have still got our work cut out for us. Apparently, “61 percent of executives say human insights should come before hard analytics when making decisions.” However, this isn’t necessarily because executives don’t want to be more data-driven; it’s just that there are still some significant barriers. So what does an analytics-empowered organization look like?
After a number of years consulting with different types of businesses in both offline and online analytics, I’ve seen several different situations and scenarios where companies are using data and analytics within their organizations. After a while you see patterns begin to emerge and you can start to make some generalizations about what differentiates companies from each other. What makes one company good at exploiting analytics and another one not?
What I’ve found is what doesn’t appear to be a factor is the company’s size or its market vertical. The fact that a company is large and has the theoretical ability to invest in analytics doesn’t mean that they do or that they do a good job of it. Big, in this case, doesn’t always mean beautiful. The same goes for market vertical. While I think that some industries are more inherently analytical than others, I don’t think it’s as simple as that.
The ability to really exploit analytics comes from a number of factors, much to do with micro factors within in the organization itself rather than more general macro factors. My list is as follows, starting with what might be considered to be the hygiene factors through to the differentiators.
Obviously data is a given, but having good data is the key here, as no one likes making decisions off dodgy data. Investments need to be made in terms of data integrity and data integration. Bad data is not fit for purpose, but good data sitting in silos isn’t either, and organizations need to think about how they maintain the integrity of their data and how they are going to leverage the various data sources off each other. When it comes to data, two plus two really does equal five, as you can get significant synergies by enriching one data source with data from another data source. So data governance and data integration are the watchwords.
Again, technology is an obvious one, isn’t it? Well, yes it is, but it’s not just about acquiring technology per se but about having the right technology to do the job and to do the job well. Too often I’ve seen organizations where the analytical technology is simply not up to the job at hand (a relatively easy problem to fix) or is completely over-engineered for the capabilities of the organization (a much harder problem to fix). There’s a fine balance between getting a system that can scale with your needs and one that is largely redundant most of the time. Particularly if you don’t have the right amount of the next key ingredient…people.
This is where the differentiators really start. Good analytics organizations have good analysts. They invest in the quantity and quality of people needed to deliver the goods. Too often I and others in the analytics community have seen organizations make strategic investments in technology without making the same strategic investments in the people to drive the technology to its best advantage. Then they wonder why they are not getting the return on investment that they expected. Analytics teams don’t have to be huge to make a huge impact but increasingly I do think that they need to be blended in terms of the skills sets needed within them and, particularly in digital analytics, a one-size-fits-all approach no longer works.
Good data, good technology, good people, but how do you turn the insights into business value? By building good processes as well. The classic optimization process for example is “test, learn, and adjust,” but this is definitely easier to say than it is to do. Good analytics organizations have figured out how to bring the insights as close to the coalface as possible to make decision-making as easy and as fast as possible. This may include integrating analytical and operations processes more closely or even completely. This may include a rethink about the way that the organization is structured and where the insight generators sit relatively to the decision-makers, or even whether the insight generators become the decision-makers. These types of decisions are major strategic ones and are unlikely to be made within the types of organizations that don’t have the right attitude, culture, and ultimately leadership.
What I and others have seen is that organizations that leverage analytics well are ones where there is someone at the top who “gets it.” This sets the tone through the department or the organization. This factor is being increasingly recognized through research into analytical organizations. Tom Davenport recognized it in his book Competing on Analytics. He classified organizations according to their analytical capabilities from the “analytically impaired” to “analytical competitors.”
The “analytical competitors” were the companies that “got it.” They were most likely to have an integrated information management strategy. They were open to new ideas and the adoption of new practices. They saw analytics as a source of innovation and competitive advantage. For them analytics is a state of mind. One of the things that characterized these organizations was executive sponsorship from the top setting the tone of the organization. Gary Loveman, chief executive (CEO) of Caesars’ Entertainment, apparently asks his employees, “Do we think or do we know?” as they’re expected to have evidence to back up their decisions. Seems like the evidence is that they may be in the minority still.
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