Over the past few years, organizations have come to embrace data as a strategic asset. In so doing, advanced analytics has emerged as a critical tool across nearly every industry sector and within almost every department in the enterprise. Outcome-based analytics can turn poor business decisions, made using haphazard guesswork, into successful ones that optimize operational efficiencies, drive revenues and provide a competitive advantage in the market.
Today’s data-driven chief analytics officers (CAOs) continually look for, and find, the means to motivate and mobilize their organization around analytics, driving analytic value to all key stakeholders and supporting staff. As they gain experience integrating these new tools with their business processes, corporate cultures and (often) internal politics, CAOs deploy game-changing tools in five main ways:
- Delivering self-service analytics to energize business users. New analytics tools are easy enough for line of business (LOB) staffers to use without help from data scientists. These tools incorporate preset algorithms for the common questions business users ask, freeing data scientists for more strategic tasks, such as creating custom algorithms or designing game-changing capabilities. More importantly, self-service analytics are upending the way decisions are made and ever-growing volumes of data are managed, from legacy databases and point-of-sale information, to social streams and sensor-supplied data emanating from the internet of things (IoT).
The ability for LoB users and others to tap into self-service analytics allows decision making to take place in near real time, thus enabling organizations to become more agile and flexible.
- Creating a view of the possible to inspire organizational/departmental leaders. The business does not perceive value until something is delivered. A proof of value (PoV) capability can help create tangible, demonstrable analytics that can meet industrialized needs or discarded if they do not create value. New analytics capabilities – once considered wasted energy – are now becoming a key factor in the success of deploying advanced analytics. Not only is the business informed of new analytics capabilities proliferating throughout the enterprise, but business stakeholders can also be invited to bring their own use cases to the party and try out their own data in a PoV environment to see what new value may emerge. Many times, even if these PoVs don’t directly solve a business need, they can provide a piece of the puzzle that ultimately inspires leaders to see new opportunities.
- Uncovering real-time insights to help the C-suite with key decision making. Someone needs to have an understanding of the corporate-level vision, ownership of analytics initiatives and access to the C-suite to drive these initiatives and tie them to the right corporate priorities. The parallel activity that the C-suite must consider along with the vision is “what happens once I get the answers to questions I have been asking?” Is there a game plan to infuse the analytics into the business process, thus making the analytics “actionable?” Armed with the right analytics tools and an understanding of how they can deliver on the corporate vision, the CAO can identify the corporate processes and resources that can turn vision into reality.
- Using advanced analytics, machine learning and AI to improve operational efficiency and team productivity. Advances in data identification, collection, integration and analysis have accelerated. New sources and types of data are being acquired and analyzed; data once embedded in e-mail, personal files and unstructured formats is being integrated with traditional/legacy (often structured) data for a more holistic view of the business or specific situation, and data-driven leaders are leveraging new tools and processes, including artificial intelligence (AI), that improve overall performance. As the CAO creates the roadmap to execute on the corporate vision, these capabilities are often introduced to create deeper insights once the business has implemented a foundation of analytics tools and processes. As machine learning and AI introduce new capabilities, the CAO is uniquely positioned to leverage these advancements to replace common business operations with automation, freeing resources to concentrate on more complex and productive corporate needs.
- Staying ahead of the technology curve to thrive in the digital age. CAOs and their teams must continuously look for new products, concepts and methods to keep their organizations out in front of the pack. This does not mean deploying untested data or every new “shiny object” in the toolkit in the hope that the business will find a use for it. The CAO must develop a “culture of learning” that drives the analytics team to obsessively research and participate in forums that promote new tools and insights, as well as embrace new technology training. The CAO and every team member should be driven to routinely ask, “How can I solve our business problems with new things I am seeing and learning?” For example, team members who keep abreast of the rise of robotic process automation (RPA) will be instrumental in helping the business thrive by automating many manual business processes. The CAO who creates an environment of encouraging and rewarding innovation will keep his organization at the forefront of technology.
As with most groundbreaking capabilities, the CAO must convince the business that this new way of gaining insights is worth the effort. The obstacles to broad adoption of these tools fall into three main areas.
- Technical: This is the most common focus area for organizations as they look to deploy emerging technology, including which BI and analytics tools to apply; whether to use a cloud, on-premise or hybrid environment; formulating a data identification, acquisition and storage strategy; and how to best cleanse and prepare data for analysis.
Some companies get locked into existing technologies that constrain them to a small set of vendors whose incremental capability improvements prove costly or insufficient. Even when vendors implement new tools or acquire companies with emerging technologies, the tools may not be fully integrated for many years or are tied to an expensive licensing arrangement, robbing the CAO of the capital expenditures needed to field truly groundbreaking analytics for the enterprise.
- Cultural: Organizations can grow frustrated by the disconnect between their real-time vision and appetite for adoption. Despite the availability of innovative analytics tools, many business users continue to use Excel because it’s what they know. Human nature tends toward the status quo unless there is a penalty for not adopting new behaviors, or the benefits of learning something new far outweigh the cost of staying with what the user already knows. In addition, many organizations are loath to change their processes, favoring outdated modes of work over creating new or improved capabilities.
Overcoming corporate inertia requires the CAO to apply behavior modification (i.e., find stimuli to motivate good behaviors) and make adoption a corporate priority by building a network of support from business leadership.
Political: Innovative analytics capabilities are disruptive by their very nature, as they create changes in processes and organizations that shift the balance of power. For all the good they bring, analytics-driven processes can also be seen as a threat by groups providing existing capabilities. The CAO must turn those threatened by these new tools into allies. A successful approach is laying out a transparent roadmap and providing opportunities for all groups to benefit by being part of the solution. CAOs also need to incorporate retraining in new technologies, gain support of the C-suite, demonstrate how all groups benefit from the new capabilities, and use an inclusive approach to roadmap design and implementation.
In today’s fast-paced business world, CAOs must be both innovative and frugal. In order to quickly analyze the vast expanse of new analytics capabilities, and gather an endless and ever-changing plethora of business requirements, CAOs cannot take on this challenge alone. By developing a cohesive partnership with the C-suite and nurturing an environment of teamwork with the business, the CAO can be well on the way to establishing the company as an analytics leader in this era of digital disruption.
Scott H. Schlesinger, Chief Analytics Officer; Cognizant Digital Business
Results oriented technology executive and recognized thought leader with more than two decades of experience and demonstrated success assisting large, global entities in driving organizational change through the leveraging of Information. Scott is the Advisory leader responsible for the growth, go to market strategy, and overall operations within the business analytics and information management practice globally.