Commercial Health & Life Sciences Viewpoint

Commercial Solutions

CREATING A BIG DATA ANALYTICS STRATEGY

Focus on immediate value, not technology strategy The use of analytics tools and techniques to gain insights from big data continues to dominate conversations across industries, driving discussions among executives in the C-suite to practitioners in the field. Senior executives and board members need chief information officers (CIOs) and chief technology officers (CTOs) to confidently advise them on whether analytics investments will lead to performance improvements and value creation. In order to do this, CIOs and CTOs must first decide where and how a deployment can deliver short-term results, and from there, develop and implement a big data strategy.

Identifying how a deployment can deliver short-term results builds the case for continued development and expansion of data management and analytics capabilities. Thus, CIOs and CTOs need to first answer a handful of questions. How can the implementation of a big data strategy deliver value in the near term and consistently over the long term? Is it complex, yet manageable? Is there a clear business purpose for using the data?

To create a big data strategy, CTOs and CIOs must take a business-oriented view. By treating data and analytics as tools with significant potential to advance business strategy, and focusing on drawing more value from these resources, an organization creates a flexible approach that can also reduce the potential for product vendor lock-in—a genuine risk as organizational needs change and new leading edge solutions continue to emerge at an increasing rate. Add to that an ever evolving and transforming technology market, and the selection decision becomes even more complex. Organizations can increase their potential to capture the value of big data by developing a broad-based analytics capability by employing four guiding principles of data science: align analytics models to business outcomes; find needed data internally and enrich it externally; encourage experimentation, learn from failure; and engineer solutions for implementation.

Additionally, change management must be a significant consideration in selecting and setting parameters for big data projects. Analytics can lead to changes at multiple organizational layers, making it especially important to articulate the rationale for technical projects to non-technologists in order to secure their buy in. Effective change management and governance strategies address the human evolution and business side of change.

The guiding principles of data science

1. Align analytics models to business outcomes

2. Find needed data internally and enrich it externally

3. Encourage experimentation, learn from failure

4. Engineer solutions for implementation

Commercial Solutions | Health & Life Sciences

Complexity (Difficulty, Technical Risk, Data Availability)

By setting objectives first, identifying short-term results, and using the four guiding principles of data science to develop a big data analytics strategy, CIOs and CTOs can create effective analytics initiatives, while laying the groundwork for ongoing enhancement and expansion of analytical capabilities.

Setting analytics objectives and prioritiesIf demonstrating value in the near term is critical to sustaining big data analytics initiatives, what types of projects hold the most promise? Exhibit 1 offers a framework for project selection based on three key criteria:

• Value. Above all, a project needs to deliver meaningful results. What is the estimated return on the project in terms of cost reduction, new revenue, productivity, or other factors as needed by the business?

• Complexity. Provocative problems may have intellectual appeal, but people don’t necessarily have the time or attention needed for them. What are the technical requirements including data availability, staff time capability and commitment, and resource allocation?

• Sponsorship. A company should only undertake a project if someone is intending to use the data, advocate for it, and make decisions based on analytical output. Does the collection and analysis of this data have a purpose?

Building the analytics case and capabilities

Research scientists at a global life sciences company wanted better tools to search for relationships between phrases being used in certain publications around the world and specific compound names.

Initially the company tackled the problem by integrating all available data and making it available to researchers. However, varying perceptions of how value is derived from data led company technologists to change their approach, focusing more on developing specific system capabilities—how data is processed and presented, how users interact with the data, and how data is leveraged for analytics. The platform the technologists created met the researchers’ immediate and particular search needs, while providing a foundation to add future tools and examine data in innovative ways. This project clearly demonstrated how big data analytics can provide near-term results by addressing specific problems while creating a foundation for future projects.

Exhibit 1. Ranking big data analytics projects

Low High

Low

H

igh

Val

ue

(Est

imat

ed R

etur

n)

Size indicates level of Business Sponsorship

Illustrative

Leveraging data to the fullestMany deployments to date have centered on implementing systems to integrate and analyze disparate organizational data. By employing hardware ranging from enterprise supercomputers to tactical platforms, data scientists have tackled business unit analytics needs in a siloed fashion. While well intended, these initiatives are often uncoordinated, overly ambitious, or lack focus, resulting in no discernable payoff. To avoid these pitfalls, and increase their ability to address business challenges through big data analytics, organizations should adhere to four key principles:

1. Align analytics models to business outcomes

Overcomplicating a problem may lead to analytic solutions that do not adequately support decision-making. Even the most complicated solutions should have a straightforward connection to business value. Clearly defining the challenges that need to be addressed can help focus analytics initiatives. Data scientists working closely with subject matter experts can help ensure that solutions and tools align with desired business outcomes and that model outputs support the decisions essential to business operation.

1Booz Allen Hamilton and Gartner, The Field Guide to Data Science, 2nd edition, 2015, http://www.slideshare.net/BoozAllen/booz-allen-field-guide-to-data-science

2. Find needed data internally and enrich it externally

Pairing your internal data with external data in alignment with specific business issues helps result in more valuable insights and better decision making. Supplementing internal data using third-party sources, such as surveys and social media data, can create a richer decision picture and support evidence-driven identification of factors affecting outcomes. Increasing data usability by 10 percent has been shown to increase productivity by 17 to 49 percent.1 Better data contributes to better models, and good starting points for securing it can include structured and unstructured sources.

3. Encourage experimentation, learn from failure

While some organizations may have little appetite for iterative trial-and-error experimentation, creating models to support improved decision making is rarely a straight forward process. Organizations can expect to fail a few times on the way to better outcomes, evaluating and refining as they go while experimenting with models such as decision trees, random forests, hidden Markov models, and support vector machines. Experimentation is encouraged, and recommended, on the path to developing the most effective analytical tools. The focus here is to quickly identify value along the path to organizational data science competency.

4. Engineer solutions for implementation

Big data analytics tools need to work at the speed of the business, if not faster. And, the most accurate models are meaningless unless they can be integrated seamlessly.

It is critical that data science teams collaborate directly with IT and business teams to produce actionable models optimized to provide value in the current operating environment. Common challenges include connecting siloed data across business lines, and modeling approaches that are too computationally intensive to be implemented at the speed of business. Policy and regulatory requirements, such as restrictions on collection and use of customer information, might guide how tools are used. Consider how the implementation environment impacts model design at the outset to guide selection of analytics approaches.

Recognizing the need for change management and governanceRealizing the value of big data analytics requires more than good analytics tools and techniques. When organizations effectively tap into the power of data to address business challenges, the results—given their siloed nature—often have significant impacts on staff and their daily activities and processes. It is therefore extremely important to establish the value proposition of any initiative early on in the process, obtain stakeholder acceptance, and establish a model for their continued engagement, in the broader context of evolving the overall organization while solving specific business problems.

Effective change management and governance strategies address the human and business side of change:

• People. Manage how staff may be impacted by the changes that will inevitably develop when an organization integrates new analytics tools and techniques. It is important to design strategies to show the value and impact of these changes (i.e., what’s in it for the stakeholders). Focus needs to be on structured engagement of stakeholders, communicating expectations and roles, seeking feedback, and showcasing early successes. With the goal of maturing organizational capability, the ability to communicate and pull those skills along to develop analytic techniques across functions becomes core to a change management program. Additionally, those who are affected by the change need to be a part of the change. Rather

Focus on value leads to giant savings

A large government financial services agency encountered instances of fraud and identity theft at unprecedented rates. Using principles of data science and working with external data science experts, the agency designed risk modeling and fraud detection solutions that resulted in over $2 billion in savings.

The agency started by working with data scientists to identify specific business problems, with the greatest potential to achieve financial ROI. These problems served as calls-to-action for their data science approach. Next, they collected data on customers from across the enterprise, as well as from publicly available data sources that carried a strong predictive power to model risk or identify fraud. While trying more than a few times (and often failing), the agency and its team of data scientists continued to make incremental improvements in model accuracy while reducing false positives of fraud. In the end, with an IT department that was heavily involved in developing a solution, the team’s final models allowed the agency to identify and prevent instances of fraud in near real-time, avoiding fraudulent payments that were estimated in the billions.

Though the models have already created significant value, the agency still seeks improvement—continuously working to reiterate the models based on new information and to identify more data sources while trying-and-failing on the way to an improved solution.

Commercial Solutions | Health & Life Sciences

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than viewing them as recipients of an outcome, they need to be integrated in the process. They should help define constraints, see the analytic outcomes as they are tested, and have a role in using their expertise in helping explain or interpret results. Through this process, they also not only accept the change, but become advocates of the change among their peers and teams as well.

• Business. Focus on structural mechanisms necessary to define, guide, monitor, and measure implementation to produce measurable outcomes.

Addressing both dimensions of change effectively alongside the implementation of a flexible governance model can ease the process of institutionalizing the use of analytics tools and techniques across the enterprise, thereby helping companies speed time to value and improve return on investment.

Realizing the value of big data analyticsLarger and larger data volumes—coupled with increasingly powerful analytics tools—are inevitable. However, there is a method to the madness, and companies must arm themselves with the knowledge and strategy to do more than just survive in the age of big data. CIOs, CTOs, and other executive leaders can improve performance and deliver results within their organizations by applying the four principles outlined above. By following these four principles, combined with appropriate change management and governance strategies, organizations can advance their analytics capabilities both in the present and for the future.