The ability to exploit analytics comes from a number of factors, including data, technology, people, processes, and leadership.
After a number of years consulting with different types of business in both offline and online analytics I've see lots of different situations and scenarios about how 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 that what appears not 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 a good job of it. Bigger, in this case, doesn't always mean better. The same goes for market vertical. I think that some industries are more inherently analytical than others, but again, I don't think it's as simple as that.
The ability to exploit analytics comes from a number of factors, much to do with micro factors within 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 data integrity and data integration. Having bad data is not fit for purpose but having 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 of 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.
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 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 they need to be blended in terms of the skill 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 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 decisioning 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 relative 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've seen is that in organizations that leverage analytics well, there is someone at the top who "gets it." This sets the scene 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," and a recent research report from MIT Sloan Management Review highlighted this. From a survey of over 2,500 respondents they identified three types of organizations; Analytical Innovators, Analytical Practitioners, and the Analytically Challenged. The Analytical Innovators represented 11 percent of the organizations they surveyed and what defined them was as follows:
"Analytical Innovators are distinguished by several key characteristics: their mind-set and culture, their actions and their outcomes."
These 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.
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Neil Mason is SVP, Customer Engagement at iJento. He is responsible for providing iJento clients with the most valuable customer insights and business benefits from iJento's digital and multichannel customer intelligence solutions.
Neil has been at the forefront of marketing analytics for over 25 years. Prior to joining iJento, Neil was Consultancy Director at Foviance, the UK's leading user experience and analytics consultancy, heading up the user experience design, research, and digital analytics practices. For the last 12 years Neil has worked predominantly in digital channels both as a marketer and as a consultant, combining a strong blend of commercial and technical understanding in the application of consumer insight to help major brands improve digital marketing performance. During this time he also served as a Director of the Web Analytics Association (DAA) for two years and currently serves as a Director Emeritus of the DAA. Neil is also a frequent speaker at conferences and events.
Neil's expertise ranges from advanced analytical techniques such as segmentation, predictive analytics, and modelling through to quantitative and qualitative customer research. Neil has a BA in Engineering from Cambridge University and an MBA and a postgraduate diploma in business and economic forecasting.
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