Three Steps to Make Smarter Business Decisions
Step 1: Put easy-to-use analytics tools into the hands of front-line decision-makers and analysts.
Step 2: Give them guardrails to ensure they adhere to company standards.
Step 3: Aim for the next level of analytics maturity.
If you’re not realizing the business value of advanced analytics, maybe it’s because your company doesn’t yet have an adequate foundation in place. There is a well-defined model for analytics maturity, but you can’t expect the benefits of maturity if you haven’t put in the work to prepare for it.
This post considers the ways in which two different organizations are turning their analytics maturity into business value. Nick Bignell, director of data science at UBS, and Brian Millrine, CIO and strategy director at Brookson Group, describe where their respective companies are on the maturity spectrum.
The importance of assessing analytics maturity
Building analytics competence into decision-making leads directly to greater profits. Companies make better, more lucrative decisions based on data and an understanding of current factors rather than on gut feel and past experience.
So, how does a company know where it stands on the scale of analytics maturity? The International Institute for Analytics has collected benchmark data over several years, which Alteryx has incorporated into an Analytics Maturity Assessment tool.
On a five-point scale, the average score across all industries is 2.2.
For that matter, some companies report more than one level of analytics maturity. UBS found that its analytics competence for the company as a whole scored 2 points. However, some departments around the bank have embraced analytics tools more readily, and their scores reached 3 points, showing greater maturity.
Brookson Group was initially focused on greater efficiency through process automation and process improvement. But as the company has moved toward analytics maturity, they’ve gone beyond the focus on efficiency and used advanced analytics to deliver better customer experiences. For Brookson Group, the assessment is valuable as a framework and point of reference to where the company has come from and where it can go next in data science and analysis. At the departmental level, business managers can use it to see where they are and where they need to go relative to their peers.
After an assessment, continuous improvement . . .
Now that both companies have conducted the assessment and seen their scores, what are they putting in place to achieve greater analytics maturity?
At UBS, Bignell has found that the biggest obstacle to maturity is getting analytics tools into the hands of users. For him, step one has been to educate users on how to develop and apply analytics and become more data-literate. With that education comes greater trust in the outcomes of what users are doing.
To that end, UBS has launched both top-down and bottom-up initiatives designed to help them on their journey toward maturity. From the top downward, their large-scale data and analytics AI program is an environment for decision-making in which the entire business can work. From the bottom upward, the company offers a review of education, ensuring that employees have the chance to upskill in data literacy. That ranges from a basic understanding of how to interpret and use charts to 13-month-long, advanced certificate programs around Python, statistical analysis, and how models work. All of that is alongside a companywide push toward automation at the department level.
Similarly, as its first step, Brookson Group embeds value through adopting analytics tools and practices into the business. Millrine believes that one characteristic of maturity is the move away from thinking in terms of dashboards. Analytics automation is the way to fundamentally change the customer experience because it goes to the core of the business value and customer proposition. The second step Brookson Group has taken is to adopt the aspect of maturity around governance and control — a necessary complement to the first step. Democratizing analytics results in an explosion of great ideas going into practice as people become suddenly empowered. The risk lies in trends like shadow IT, with people outside the line of business operating important processes. So the second step toward maturity is to put in place guardrails for managing that risk.
. . . and scaling up analytics
Once you’ve defined a path to greater maturity, how do you scale analytics to drive value for the entire company?
With thousands of users, UBS sees scaling up as a function of lowering the barriers to entry. One key to scaling up is to give users what they need to upskill themselves. That includes making it easy for users to get hold of self-service analytics tools like Alteryx, to use the tool with proper governance, and to be comfortable with the training materials.
The other key is to identify enthusiastic individuals in each area and groom them. Those champions figure out how to use the tools to, say, save themselves a few hours per week — or per day — then spread the word to their colleagues. Champions tend to create a community across the organization, so getting them together regularly results in a spontaneous cross-pollination of ideas.
UBS has found champions to be crucial for proving the value of the tools to mid-level business managers and holders of the purse strings within the organization. Why is that important? Because it’s not in the objectives of mid-level managers to make the company more data-driven. Their objective is to deliver a given service at a given cost, and they have little incentive to look at it any other way. That’s why it’s important to have both the top-down message that the company has to be data-driven and the bottom-up chatter of champions talking about analytics tools.
Maturity and resilience in uncertain economic conditions
How can analytics maturity ease the transition from one global economic shake-up to the next?
Brookson Group points to the versatility of the analytics-mature company, with two modes of developing solutions: high-velocity innovation and the traditional waterfall style marked by high governance. Millrine describes his company’s program of democratizing analytics and encouraging innovation among their citizen data scientists. At the same time, Brookson Group has built a dedicated developer team by taking people out of the business and allowing them to be full-time developers with Alteryx. The developer team staffs a center of excellence, conducting checks and governance on the work of the fast-moving citizens.
At UBS, investment bankers found that the IT applications they traditionally used to manage risk could not cope with the sudden change in liquidity as the pandemic began. They turned to analytics tools like Alteryx and built their own apps to navigate the wild volatility of financial markets. If they had gone through traditional IT processes, they’d have faced development queues measured in months or years. When managers and analysts can solve their own problems like that, it blurs the lines between IT and the business. Decisions get made, and things get done in a very quick and agile manner.
Both companies emphasize the lesson of automation in times of economic uncertainty: Automating analytics does not always mean people are going to lose their job. Those employees are freed up to become much more productive, so they add more value back to the company. Instead of spending their whole week on the same manual tasks they’ve been discharging for years, they have the opportunity to make the business more competitive. In a severe economic downturn, that can spell the difference between survival and extinction.
Recommendations and steps
As described above, step one for these companies is democratization and getting people to use self-service analytics tools like Alteryx. With high adoption, people start finding business value all around the organization. That leads to step two, which is to apply governance in a center of excellence to mitigate the risk of rapid innovation with checks.
By the time that has become a problem, the business has seen the value in the tools and in the approach to being data-driven. Excitement ensues, leading to step three, which is to look at the next level and think about the kind of analytics to undertake. Depending on the maturity rating, the company can embark on programs for predictive modeling, data science, or AI, which are attainable from a solid foundation.
Are you ready to see how your company’s analytics effort scores?
Find out more and take the Analytics Maturity Assessment developed by the International Institute for Analytics in partnership with Alteryx.
And watch the Brookson Group and UBS webinar for more insights into boosting the analytics maturity of your own organization.