Analytics has been a key area of discussion in corporate meetings for many years now. Leaders across industries recognize that, in today’s competitive landscape, insight-driven decisions are the key to developing a competitive advantage. However, the use of analytics to create a competitive advantage is about to become even more widespread. The increased growth in data sources and the ubiquitous nature of analytics is creating an environment where robust analytics capabilities are needed just to keep pace. To prepare for this environment and try to attain the most value from their data, many companies are evolving beyond just using analytics and are transforming themselves to build an insight-powered enterprise.
Many companies are accelerating their investments in analytics capabilities and strategies. Companies are establishing a center of excellence for analytics led by a Chief Data and Analytics Officer (CDAO). Companies are also creating agile analytics governance and value realization offices to help ensure that they are getting an appropriate return on analytics investments. In addition, they are paying close attention to talent sourcing and planning as talent is in short supply.
They are focusing on faster deployment of new analytics capabilities and tools by piloting with the intent to scale. Finally, they are focused on raising the analytics IQ of the entire organization through training programs and new ways of working, such as using immersive analytics environments.
This focus on building analytics capabilities can increase as companies come under pressure to demonstrate the value from analytics. High performing companies are embedding predictive analytics insights into key business processes twice as much as low performers. They are winning the competition for analytics talent through multi-pronged talent sourcing strategies, they are leveraging more advanced tools and techniques, and investing at a much higher rate. In fact, a lot of the high performers plan to significantly increase their analytics investment over the next three years, compared to a very small percentage for the low performers. This is creating a competitive landscape of “haves” and “have nots” in which many companies are undertaking major transformational initiatives to catch up with the high performers who have been building their capabilities for years.
Here are 5 steps to bring analytics into an organization:
Step 1: Establish a Center of Excellence
Companies with high maturity are more likely to have established a Center of Excellence (CoE) for analytics activities with a concentration of talent and resources.
Organizations need to focus their strategy and planning activities, around activities such as analytics modeling and structured reporting, as well as strategic functions like supplier management, talent planning, portfolio governance, and outcome tracking. Establishing a center of excellence for key resources builds capabilities, but it also supports consistency and high standards while allowing functional resources to focus on key business problems and applying insights.
While this trend continues, companies also realize that it is important to counterbalance this collection of specialized resources with local decision-making closely connected to the business functions. Some companies distribute certain activities back out to the business functions once the standardized processes are in place. This is similar to the path a large bank, an early adopter of analytics, has taken over the course of its journey. The company needed to put people with data analysis skills close to the business operations to solve key problems. This was after they had spent years creating a center of excellence focused on building foundational analytics capabilities.
Chief Data and Analytics Officers
Leadership is another key element. A lot of companies are elevating analytics by creating a Chief Data and Analytics Officer (CDAO) role responsible for both the vision and the implementation of the enterprise analytics strategies.
The CDAO is often responsible for developing goals, strategies and plans to support the information, reporting and analytical needs of the company, but also acts as an agent to change the analytics culture of the company. One of the CDAO’s biggest challenges is how to design an effective operating model that will convey this change throughout the organization. CDAOs of large, high performing organizations find that the analytics journey used to be an out-of- body experience that most struggled to understand, while today analytics is an in-body experience that needs to become part of the cultural DNA.
Step 2: Create Agile governance
Traditional governance models are often thought of as sluggish, with a focus on standards and processes, but today, high performing analytics leaders are building thin, horizontal governance structures, focused on outcomes and speed to value, rather than creating hierarchies that are slow to adapt.
These structures take a “test and learn” approach to rolling out new capabilities. They establish success criteria, with regular checkpoints for measuring performance against these criteria. They also employ “fail fast” techniques, rapidly rolling out new ideas and capabilities and testing them repeatedly. If an idea is not working, it is dropped quickly, so that the company does not continue to invest in something that does not add value.
Another element of agile governance is a structured innovation process. Leaders may set up innovation or “SWAT teams” with a mandate to focus on key business questions for a concentrated period, often as short as 30 days. Or, they may use a form of open sourcing; they could establish an Innovation Forum through which everyone in the organization (including contractors) could submit ideas for solutions to problems posed by senior leadership. The leadership team can choose the best idea at the end of each designated period and provide resources for the winner to go forward and build out the analytics solution.
Perhaps the most critical part of establishing an agile governance structure is ensuring that it uses the key metrics that are most important to the business. To this end, CDAOs are designing executive scorecards to track performance against these key metrics, which might include:
- Speed to standing up priority capabilities—How quickly was an initiative tested and/or piloted
- Pace of adopting new capabilities— Identifying and tracking the adoption of priority capabilities, and compressing the time from pilot to industrialization
- Value realization—Defining overall value delivered through the analytics initiative or project
Value Creation Offices
Leaders are also instituting value creation “offices” to spearhead outcome tracking against key metrics and to ensure that the value from analytics is realized. These “offices” have involvement from senior leaders with accountability for analytics as well as program management office resources who can develop templates and processes as needed. Some of the key success factors for these offices include their ability to design and implement a closed loop process for identifying and measuring the value of each analytics project or initiative. If the analytics initiative fails or does not meet the identified baseline value, the closed loop process should incorporate that learning back into future cycles.
Step 3: Create an inter-disciplinary, high-performing analytics team
Companies with advanced analytics capabilities field teams with diverse skills. Talent is organized effectively and there are innovative programs to keep the best talent engaged. These companies use multiple talent sourcing options, including, but not limited to, internal development and hiring, but they are also attracting more talent from other companies.
Analytics Pod Structures
It remains difficult, if not impossible, to find all the skills required for analytics success within any one individual. Many mature companies, therefore, are establishing “pod” teams which have a mix of roles, including data scientist, analytics modeler, visualization expert, data engineer, business analyst and business domain expert. By combining these capabilities, analytics pods can take an integrated view of business problems.
More mature companies have a greater percentage of modeling and discovery resources aligned with a standalone analytics function. However, skill requirements change as analytics capabilities mature; companies in the middle stage of analytics maturity call for more data modeling and stewardship, while mature companies—who are past the core challenges—need more data scientists and other specialized roles.
Analytics Career Paths
Analytics talent tends to follow non- traditional career paths in response to the value that this talent tends to place on becoming subject matter experts in finding insights in large data sets, often instead of following traditional managerial career paths. A lot of companies are encountering similar talent planning challenges and are responding with a variety of methods including varied work assignments and compensation based on technical competency.
Leading analytics companies create mechanisms to source the best talent for their organization. They may, for example, build their brands internally and externally, marketing themselves as the destination for top talent. Companies use crowdsourcing, sponsoring competitions and offering prizes to solve problems. Still others run open challenges or “hackathons” that provide incentives for people to respond to and solve business problems.
Some companies even partner with academia to source talent. They can partner with leading universities to open data analytics centers. These labs can focus on data analytics, developing data research and innovation to solve problems ranging from assortment optimization, social media and market trends to large- scale data initiatives.
Analytics talent is in short supply. Given that there does not appear to be a solution to the talent shortage in the near future, it is important for analytics organizations to focus on talent retention by developing reward and incentive programs that keep these individuals engaged. We have seen that a key to retention is keeping the analytics resources challenged. Many of the financial services companies are seeing a boomerang effect as analytics talent left to go to high tech companies in the last 5-7 years and that talent is now returning to Financial Services as the business problem set to solve is more interesting and complex than at the high-tech companies, which keeps the advanced analytics talent engaged and constantly learning. This engagement has resulted in higher retention over time.
Step 4: Deploy new capabilities faster
One of the biggest challenges for analytics organizations is to establish an operating model with a view to scaling priority capabilities, especially considering the roadblocks posed by existing analytics skills and in-place data architectures. Leading analytics organizations deploy new, agile technologies, as well as hybrid architectures and specifically designed toolsets, to help achieve speed to capability and desired outcomes. One distinguishing characteristic of fast-moving companies is that they pilot with the intent to scale; that is, they establish the right mindset, processes and accountability in advance, then move quickly to test, learn, refine and implement.
Scaling priority capabilities requires new approaches and mindsets for many organizations. These organizations may need to “unlearn” what has already been learned; for example, they may need to bring the data to the analytics, rather than the other way around. There may also be differences in the way teams with statistical backgrounds tackle scaling problems, using a hypothesis/test/verify framework.
Many organizations have already begun addressing these issues. By prioritizing the scaling of capabilities, companies can optimize analytics investments, rationalize vendors and suppliers, improve talent acquisition, and rationalize data and tools.
The consumer banking operation of a major multinational bank was suffering drop-offs in their net promoter scores and wanted to understand why. They had a very complex and convoluted technology environment with a slow, costly and inflexible legacy infrastructure. It took on average 9-12 months to develop and deploy new models (for example, credit risk scoring for loans or credit line increases). The bank had a vision of leveraging analytics to improve their overall customer experience yet they needed the technical capability to enable this strategy. They also wanted to leverage big data technologies as an alternative to traditional approaches for both speed to capability and cost benefits.
They did a data and analytics strategy evaluation leading to a roadmap for them to become more dynamic and more real-time, and to improve the customer experience. The first area of focus was to revamp the technology infrastructure so that the bank could achieve desired outcomes. The next step was to develop a data library using open source technology rules engines for the real-time scoring of credit line increases for customers. This helped produce an increase in annual operating income.
With the data and rules engine, the bank could identify the cause of the drop-off in customer satisfaction—and it was not what they had thought. The most affluent customer segments were the most digitally savvy, and they were dissatisfied with their experience on the Web and on mobile devices. The bank had thought that service fees were the issue, and while service fees were an issue across all segments, the key issue for the most profitable customers was the online and mobile experience.
The bank is now moving towards an “always on” capability to improve the customer experience and thereby improve customer retention and profitability. By leveraging an inter-disciplinary blend of skills— data scientists, visualization experts, data architects, and business domain experts—the bank could understand the insights and act quicker to design and implement solutions. This innovative project at the core of the bank’s operating system helped the bank realize a faster return on its technology and analytics investments.
Step 5: Raise the company’s analytics IQ
The final distinguishing characteristic of leading analytics organizations is their commitment to raising the “analytics IQ” of all roles within the enterprise.
They may, for example, implement an Analytics Academy that provides analytics training for functional business resources in addition to the core management training programs. Within the analytics organization, the Academy may focus on developing business and communications skills to make sure that the insights obtained are put to good use within the business units. And it may provide training courses to raise the business and analytical acumen of the IT organization.
The Intelligent Enterprise
Leading analytics organizations have a vision of what might be termed the “intelligent enterprise.” They are training resources to use new tools and techniques to improve decision-making throughout the enterprise and they are also implementing innovative technologies such as advanced data visualization to communicate the value of analytics to business units, core functional teams and IT.
Some companies build their own immersive environments that leverage visualization techniques to provide greater context for the information presented as well as the trends that are being illustrated. They are moving away from traditional presentation tools and are leveraging more interactive tools to improve collaboration. To encourage use of these new interactive tools, these companies might set up contests where resources can earn badges and certifications based on insights developed and actions taking within the tool.
This holistic, interactive learning approach where business, analytics, and IT resources are equipped with new immersive tools, techniques, and formal training opportunities allows companies to:
- Activate leaders by focusing time, funding and attention on desired behaviors and skills;
- Shift behaviors and mindsets to engrain new habits, and
- Embed behaviors into business processes and metrics, all in service of driving a cultural transformation.