Think fast. You have an opportunity to buy a portfolio that could help your organization successfully enter a new market. Expanding into this market will give you an important strategic advantage over your competition and potentially open doors to other expansion opportunities. Upon preliminary review, it’s unclear whether the portfolio is in line with your current risk strategy and managing it might overwhelm your systems. This could both jeopardize your opportunity and add risk to your existing portfolio. It’s a big bet and you have a limited amount of time before the offer expires and the portfolio is offered to your competitor. How do you decide what to do?
There’s more than one way to approach this opportunity — and going with your gut isn’t one of them. Given the magnitude and complexity of the decision at hand, advanced data analytics must factor into your decision-making. But do you have the necessary organizational analytics foundation to make an informed decision? Have your key stakeholders fully bought in? Do you have the right team? Are you able to effectively access, manage, analyze, govern, and put your data to use? Understanding these common challenges and taking a pragmatic approach to overcome them can set your organization up for long-term success.
The proliferation and accelerated adoption of digital commerce and communications has triggered a data explosion. It's estimated that 2.5 quintillion bytes of data are created every day and businesses collect great volumes and varieties of data with every transaction, interaction, website visit, and social media reference. This data holds vitally important information to help financial institutions, for example, guide decision-making, fraud detection, marketing strategy, etc. Yet despite the widespread advances in technology and techniques, data teams continue to struggle with even the most basic functions, including wrangling, cleaning, and preparing the data for analysis. These and other challenges are major obstacles to harvesting, let alone using, the critical insights within the data. While seemingly obvious, it’s important to understand these issues and why they’re persistent so you can figure out how to work with — or around — them.
The biggest and most important challenge to overcome is organizational buy-in, typically in the form of resource allocation. Many businesses continue to be driven by legacy visions and values, intuitive decision-making, or both. They have not (yet) embraced data analytics as a strategic priority and typically view data management as a production group, under the oversight of IT.
While your organization may face some, all or none of these challenges, the return on your analytics investment (e.g., data, technology, people) will be hard to calculate, or even envision, without a clear articulation of what you aim to achieve. Starting with the end in mind provides the basis for a strategy for how to get there by knowing where to focus and what to fix. It helps you articulate what’s important, what you need, and sets the stage for gaining the support and resources to achieve your goal. What stands in your way?
Make a business case by helping stakeholders better understand the value advanced analytics can deliver. Build your approach around use cases aligned with business priorities and strategy to win the attention and interest of the leaders responsible for delivering business results. Here are a few examples where analytics can be of use:
To derive the insights necessary for decision-making, organizations need to have the right mix of data science, analytics, and business intelligence experts with a balance of roles and skills. Market demand for competent data professionals was predicted as early as 2012 when venture capitalists (VC) were worried enough about the tight data labor pool, they built specialized recruiting teams to feed their businesses. Supply continues to outpace demand with businesses of all sizes facing pressure to attract and retain this talent. According to one survey, 36% of respondents cited recruiting and retaining data scientists as the biggest challenge to achieving their company’s AI goals, besting both inadequate technology (28%) and insufficient data for model training and accuracy (26%).
Upskilling current employees and hiring to fill gaps is an important component of building a data-driven culture and organization but it is a process that requires commitment, discipline, time, and money. Alternative ways to reinforce your analytics capabilities and contributions include process automation and outsourcing expertise. Advanced analytics consultants, for example, can be an expedient and efficient way to supplement and fill knowledge and skills gaps.
Like the links of a chain, data-driven decisions are only as strong as the weakest datasets used to make those decisions. Data integrity — the property that data has not been altered in an unauthorized manner — is essential for reliability. If the data is corrupted, modified, or destroyed, integrity is compromised and trust in the data will be lost. The downstream repercussions of decisions made based on the compromised data could be extensive and enduring.
The chart below is more than seven years old but still representative of how data scientists spend their time. Numerous articles and studies continue to report data scientists spend 60% to 80% of their time collecting and cleaning data rather than analyzing them.
Much of the reason for this is that organizational data is typically managed in one of two ways, and both require substantial time and resources to cleanse, transform, catalogue, curate and manage the data before proper analysis can even begin:
Unless (or until) you can implement a safe and sustainable process for data acquisition, cleansing, transformation, cataloging, curation, and management, consider engaging a data provider to help facilitate the process. Investing in creating and securing a clean, robust data source should yield better returns than the swamps and silos.
As reliance on data increases, so do the demands on analytics software and platforms. If an organization’s analytics technology is outdated, unscalable or not able to integrate with the necessary solutions, for example AI, organizations face a potential brick wall as systems approach capacity.
Depending on your goals and resources, your technology needs can be met in several ways. One smart option is to partner with an analytics technology provider that can scale their solution(s) to meet your needs in the short- and long-term. Even with limited resources, there are multiple benefits to third-party support, including the ability to access the latest technologies and techniques with relatively minimal incremental investment.
Your data governance strategy needs to safeguard your data as a strategic asset, not a liability. Studies have shown only 38% of financial organizations globally are ready to handle the risk associated with the safety of the data they have in their systems. And according to the Data Governance Institute, “the most overlooked aspects of data governance are the communication skills of staff who sit at ground zero for data-related concerns and decisions. They need to be able to articulate diverse stakeholders’ needs and concerns and to describe them in many vehicles and mediums. They often need help learning data-specific communication skills to ensure all stakeholders get the right level of information at the right time, in the right sequence.”
Installing an internal board to administer and manage enterprise data governance can help initiate effective communication and mitigate a wide range of data-related risks and inefficiencies, enabling your organization to harness the full potential of its data while reducing potential downsides. Consider the following when creating your board:
Craft a charter that outlines your board’s mission, objectives, and authority
Bring in experts from IT, legal, compliance and other key business units to ensure a well-rounded perspective:
Despite these enduring challenges, the development and adoption of analytics technology and techniques accelerates, as does demand for enterprise-wide, data-driven decision-making. Businesses need to find their way forward. Every organization has its unique set of needs, capabilities, and resources and very few, if any, can do it all. To achieve strategic goals and make a true competitive difference, most need to look outside their four walls for the analytic power to make it happen.
Partnering with the right provider is critical to the success of your endeavor. TransUnion has the data, expertise, and technology you need to succeed — and a reputation for helping consumers and organizations build trust and transact with confidence. Our world class data scientists, consultants and adaptive analytics infrastructure enable us to swiftly assess and advise on the best combination of methods to support your use case(s).