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An organization needs to have a strong data foundation in order to utilize data and to apply it for decision-making. As more companies recognize the need for data literate teams, they must address key gaps in their existing digital transformation model.
With the advent of data-driven innovation in recent years, it has become vital for businesses to evolve their data science competencies and tools. Data skills have been shown to not only drive tangible performance benefits, but also to yield competitive advantages in an increasingly digital market. However, there are challenges to building a data-literate organization, particularly at large scales and across varied business functions. This paper offers guidance on how companies can invest in enterprise-wide data fluency and avoid common pitfalls related to the practice of data analysis and data modelling.
Data fluency, also known as data literacy, is the ability to communicate data insights while understanding the context and methods used to process the information. From finance to healthcare, the growing availability of data in many sectors has accelerated demand for analytical skills across all employees. Many companies within these spheres can attest to the prevalence of AI, machine learning, and other such tools across their business landscapes. However, without the capacity to interpret and speak the “language” of these technologies, they are unable to maximize added business value. The skills gap can further obstruct efforts to digitize, and Gartner’s Annual Chief Data Officer Survey found that “data literacy” was frequently cited as a major roadblock to implementing data-driven initiatives (Gartner, 2020). An organization must therefore have a strong data foundation in order to utilize data and to apply it for decision-making workflows.
As more companies recognize the need for data literate teams, they must address key gaps in their existing digital transformation model. This can be achieved in a series of steps, from investing in data science programs to teaching individuals additional technical knowledge. Moreover, these capabilities should be cultivated holistically, in a way that improves the organization overall. The outcome of data fluency is not restricted to just data scientists, but instead allows all users to acquire “the appropriate level of data skills to work efficiently and effectively based on the different needs of each job role” (Datacamp, 2019). This cultivates growth that is both scalable and high-impact for the whole enterprise.
In practice, there are four advantages of developing a data fluent organizational environment:
Many organizations, including notable players such as Uber and Amazon, have embarked on their disruptive journeys by first recognizing the need for digital transformation in the modern economy. This is especially apparent as Covid-19 has accelerated the digitization of day to day processes. However, 70% of digital transformation initiatives end up falling short of their established goals. A crucial reason for this failure is the inability to identify sustainable enterprise-wide data skills, which is a prerequisite for successful digital transformation. For example, Gartner claims that less than 50% of organization strategies have raised data analytics as a driving force for delivering business value, and Forrester estimates that between 60% to 73% of corporate data is not considered for analysis. Without the recognition and evaluation of these existing gaps, companies will struggle to reach a higher level of data maturity. Therefore, the first step toward building a data foundation starts with an assessment of the current state, which includes the existing data infrastructure and level of data competency across business units. It is important to address key questions at this stage, such as:
This will help capture the baseline of employee proficiency, along with the firm’s overall digital transformation readiness. At the same time, organizations need to be aware of their strategic goals as they pertain to data analytics and data fluency. These assessments should not be performed in isolation, but within the context of overall business objectives.
In a data-oriented organization, data fluency can address a variety of problems and answer even highly specialized questions. For instance, a financial analyst could succeed in using data to optimize and improve their annual spend, while a business analyst might be able to visualize the logistics and provide meaningful insights. This is fundamentally an inclusive strategy; its success hinges on leveraging suitable data skills across different levels and functions of the firm.
In order to become data fluent and to bridge the gap of untouched data, a useful approach would be to “upskill” users. Prior to this stage, the initial assessment will have provided an overview of internal analytics capabilities and identified areas for potential improvement. This process then sets the stage for an upskilling roadmap, which can take the form of targeted learning programs or broader training workshops. In particular, it is important to take into account which business units require training as well as the level of knowledge to be imparted. Not everyone needs to know sophisticated programming languages in their day-to-day activities. An effective data literacy effort should be role-driven and just as importantly, worth the time spent.
Several companies have already invested in upskilling their employees in an effort to engage with the digital age. For example, AT&T recently initiated a $1 billion, 10 year long project to upskill about 125,000 of their employees. Airbnb similarly advanced its own private Data University that introduced basic skills to the organization and taught them to make accurate, data-driven decisions. One Mckinsey study established that 66% of companies have or are proposing upskilling endeavors to address data skill gaps. Across the movement for data literacy, firms generally maintain varying degrees of investment. Whereas some organizations have launched large upskilling programs, others may benefit from a more incremental process, piloting exercises with small groups before scaling up. Regardless of the strategy, this highlights another cornerstone of a successful data fluency program: the culture must be conducive to digital innovation and in turn, to adopting new approaches for learning.
The path to data literacy has its strengths as well as challenges. For many organizations, the benefits are well-known—increased productivity, competitive advantage—while the risks are less defined. In upskilling the existing workforce to become more data fluent, it is important to note that no single set of skills will suffice for each person. Just as universities offer different academic tracks, organizations should build out programs that service varying roles and departments. Beyond that, companies looking to foster data talent must also start at the leadership level. There needs to be participants beyond just the data science practice, capable of promoting and sustaining this transformation (Harvard Business Review). Firms that are able to prepare well-rounded data programs and initiate change at the top-most levels will be better positioned for success.
There are a few main takeaways regarding the importance of data fluency and building a data foundation:
Sia Partners is familiar with the types of challenges facing clients as they embark on their digital transformation journey. We have experience providing both data science services and business expertise across multiple sectors, from Banking to Energy & Utilities. From a technology perspective, our work is driven by open innovation and Artificial Intelligence—all supported by our network of over 150 data scientists, data engineers, and web developers.
In addition to ready-to-use automation tools and data science solutions, Sia Partners has also developed a Digital Transformation Assessment that provides clients with a roadmap for business transformation.
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|Offer global coverage across 30 offices and 16 business units. Services span Compliance, Cybersecurity, and Supply Chain & Logistics.||Facilitate the production and industrialization of AI projects.||Help clients navigate their digital transformation journey by performing maturity curve assessment, in which data literacy is a component of the data readiness stage.|
|Deliver enhanced results by equipping SMEs with a catalog of AI solutions, datasets, and models.||Develop internal innovation resources, such as STUDIO, our startup investment arm, and assist clients with the development of innovative ecosystems.||Drill down into the critical questions that need to be asked in order for firms to take their next digital steps.|
|Support client through entire data value chain, from data gathering to data modeling and deployment.||Offer customized recommendations that allow clients to capitalize on transformation opportunities.|