This is part 13 of our series on the findings and text from the IBM Institute for Business Value’s latest study and paper “Analytics: A blueprint for value - Converting big data and analytics insights into results” from my colleagues Fred Balboni, Glenn Finch, Cathy Rodenbeck Reese and Rebecca Shockley.
In our last post, part 12, we explored establishing a business-driven agenda for analytics, and discussed activities within each of three Strategy levers: Sponsorship, Source of value and Funding. In today's discussion, we will look at the three levers of Technology that help achieve business goals through effective application of technology.
Enrich the core analytics platform and capabilities
Most firms will need to enrich the core analytics platform and capabilities available within their organization to manage, analyze and act on the insights that will deliver value from data and analytics.
Effective use of technology to achieve an organization’s strategic goals begins with a strong analytic talent pool: individuals who understand the business or agency day-to-day operations and challenges and can combine that knowledge with analytics to create viable insights that deliver positive business results.
To effectively put this talent to use, organizations need to govern data assets with rigor and create a simplified, more flexible hardware infrastructure. And when making decisions relating to their IT infrastructure, they need to look toward their future needs as well, and architect for the future growth and needs of the organization.
Following (and summarized in Figure 16) are recommended actions to help organizations gain the capabilities associated with the Technology levers of Expertise, Data and Platform:
Leaders have discovered it is more effective and cost-efficient to supplement business knowledge with analytics knowledge by building skills among those already within an organization. Organizations will likely find it easier to teach critical thinking and analytics software skills to someone knowledgeable about the business than to instill business knowledge in an outside analytics expert. Training existing employees is also prudent given the current low supply of, and high demand for, strong analytic talent.
Organizations can proactively create opportunities for knowledge sharing through cross-project pollination and co-location within analytics centers of competency, which offer mentoring and knowledge-sharing opportunities at the grassroots level. Ongoing learning opportunities, ranging from formal training to lunch-and-learn sessions, enable skills to grow organically.
Top-tier organizations establish a formal career path for analytics professionals to foster their professional development needs through a formal structure of training, career planning and incentives. By formalizing the role, executives send a clear message that data management and analytics are not secondary or incidental to achieving the enterprise’s strategy, but integral to it.
For those complex data management or advanced mathematics skills that cannot be developed internally, we suggest organizations use partners to supplement skills gaps. A significant portion of respondents indicated an inability to find and hire needed skills, an issue readily confirmed by message boards at analytics conferences around the globe.
Indeed, a growing number of executives are determining that managing and training analytics experts are not core competencies for their organization, or cannot be due to the lack of available skills, yet recognize analytics as a fundamental part of their business strategy. Innovative public-private partnerships are emerging around the globe to create centers of analytics excellence accessible to the broader business community. Whereas outsourcing data management and software development has been available for decades, these new centers offer the opportunity for companies to overcome the global analytics skills shortage and gain access to the capabilities and insights needed to achieve their target strategic outcomes.
Organizations that govern data with rigor not only enable cross-silo data sharing, but instill confidence in the data and allow the organization to make data more widely available and accessible. In addition to protecting customer data, strong security – ironically – also enables wider sharing of data within an organization. Once sensitive data is secured through such practices as role-based access, data masking and monitoring, sharing the data becomes less risky. Increasing the availability and access to data, along with empowering the end user, drives data and analytics usage.
Most organizations have experienced the difficulty involved in integrating disparate data stores into a cohesive enterprise asset. Leaders have learned the most effective way to enable enterprise-wide data sharing and a single view of the customer is through a set of data management standards that establish uniformity in the data where needed, yet are flexible enough for business units to conduct their own analysis.
These standards create more than a foundation for master data management, though. Strong governance creates a direct path to improve data quality, accessibility and availability. Traceability and transparency in a data’s lineage enable analysts and executives from across the organization to understand where the data came from, how it has been processed and what it means; this enables a level of trust that only comes with clarity. Metadata management, a key part of rigorous data governance, is a step in the right direction for organizations crippled by a reluctance to manage data as a strategic asset.
The need for rigorous governance is intertwined with the organizational need to capture a variety of big data sources. The vast amount of customer data available in social media feeds, videos, text chats and other unstructured data cannot be ignored due to the sheer potential insights it holds. However, enabling a greater understanding of customers and their behavior patterns comes with a responsibility to protect the privacy and security of that data.
Part of moving the focus from operations to innovation is to rethink what the organization needs today in terms of integrated hardware and software capabilities—to rethink not only what is needed to solve key business challenges, but what core capabilities the organization needs to provide itself, what it needs to have physically onsite and what, if anything, could be provided by newer technologies, outside vendors or business partners.
After years of countless mergers and acquisitions, many organizations find themselves with a complex, pieced-together environment that often struggles to deliver consistent and complete information. With today’s “need for speed,” organizations can simplify and modernize the existing platform by taking such actions as creating reusable extract-transform-load (ETL) components and reducing data duplication and the number of data model tables by moving to an industry data model. This simplification results in data that is easier and more efficient to store, manage and access.
Organizations that increase reusability in application development and maintenance slowly transform their environments over time using their existing IT budgets. Teams build and deploy reusable ETL components, replacing long, repetitive ETL jobs with sets of target-based jobs, maximizing resource utilization, reducing the cost of maintenance and decreasing delivery time of future data integration projects. This allows the organization to do more analytics with within a pervasive environment of flat budget constraints.
Additionally, the reduction in complexity of the analytics environment makes it easier for the organization to integrate and pilot new technologies that enable business capabilities. Leaders are adopting the newer technologies at a measured pace, evaluating each before implementing, including cloud, big data, mobile capabilities and managed shared services constructs. Defining the right business use cases and pairing them with the right proofs of concepts, prototypes and pilots is an essential step in enabling new business capabilities.
In our next and final post in this series, part 14, we will discuss driving change with analytics, and express that senior leaders must consider driving change with analytics a core competency within their organizations.
Catch up on the entire series so far with parts one through twelve:
- Part one
- Part two
- Part three
- Part four
- Part five
- Part six
- Part seven
- Part eight
- Part nine
- Part ten
- Part eleven
- Part twelve