Tag: Data Literacy

A Five Step Framework for Data Literacy | Blog

In our first blog on data literacy, we highlighted the importance of moving from intuition-based to data-driven decision making and the benefits enterprises can accrue over time by establishing a data-first culture.

However, operationalizing data literacy within an organization is fraught with challenges that are rooted in legacy systems and people. So, we developed a framework that enterprises can embrace to embark on their journey towards becoming “data literate.”

Exhibit 1: Operationalizing data literacy

This framework helps enterprises uncover the value of data, democratize access to tools, and achieve scaled adoption of a data-driven culture across businesses and subfunctions. Here’s a quick look at the steps.

1. 1. UNCOVER: Enterprises should start with uncovering the datasets that will play a key role in decision making at various levels. This may include both internal and external data sets. While internal data will provide insights into the company’s business and day-to-day operations, external data or third-party data sets will provide added intelligence to deliver a holistic view of a given problem statement. For example, data collected through the company’s CRM platform, coupled with external data on a target audience across their digital avatars, will help the company better target/personalize their marketing communications across channels. To run this phase successfully, enterprises need to appoint data aggregators. Their key role will be to keep an account of the data that exists within the organization beyond the usual ERP systems. They will also focus on collating relevant data sets from external sources. This role will be supported by analytical systems running algorithms on historical data to find the best custom fit data source for standard decisions.

1. 1. APPRAISE: Uncovering data won’t yield benefits unless the data is appraised. Data appraisal has two aspects: data quality and access control. For business users to trust the data and the quality of data-based insights, enterprises need to ensure that the data is accurate, complete, consistent, reliable, and up to date. So, having the systems and processes in place for data quality management is key to enabling data literacy. For this, organizations will need to appoint data stewards who will be responsible for incorporating processes, policies, guidelines, and responsibilities for administering all of the organization’s data. They will need to be assisted by automated data quality and metadata management solutions. The future goal of any enterprise should be to equip all data owners with the right skills and tools to ensure data quality and policy upkeep.

1. 1. DEMOCRATIZE: Enterprises will only be able to glean value from their data if it’s in the right hands. They will need to identify the right stakeholders equipped with the right tools and skills who will be able to manipulate data to drive decisions. So, data democratization will be key to building a data-driven culture. However, governance and access control to ensure the right data reaches the right people will form a key pillar of this democratization. Here is where a data marketplace comes in. An enterprise-wide data marketplace will have external data sellers as well as the enterprise’s own data aggregators who will pour in the data through data stewards. Buyers will be the business users who will consume it to derive insights. A catalogued and secure marketplace within enterprises will become the most valuable asset.

1. ENGAGE: Once the right dataset reaches the desired stakeholder, the stakeholder is responsible for deriving insights from it. So, enterprises must invest in developing data skills for the right set of business users.  The development plan for business users should focus on two different aspects of training:
   • Technical training focused on developing an understanding of data types (internal vs. external), data characteristics (formats, features), data applications (analytics, artificial intelligence, decision support), data techniques (pattern discovery, pattern recognition, prediction), etc.
   • Non-technical training focused on training business users to “know, speak, and argue” with data by instilling data communication as a core part of their day-to-day activities.Enterprises should focus on upskilling their workforce through a robust learning program and investing in learning assets to promote ongoing development.
2. SCALE: The first four steps are bound to reap benefits. But they may be short-lived unless organizations commit themselves to scaled adoption across functions and organizational levels. Companies will need to integrate the best practices that individuals who have achieved success use and create a conducive environment for others to follow suit. This may be achieved by developing and nurturing communities and forums within organizations to encourage data-driven decision making. It will also be important to identify specific data champions who would be the voice for change. These five steps, along with a data-first culture, will become the backbone of a data literate organization, promoting data literacy across the board to help transform into an intelligent enterprise. The change will need to be supported by a fit-for-purpose partner strategy to navigate the tools and technologies needed and the associated business, process, people, and technology change management.

If you have any questions about how data literacy can help your organization tackle complex situations, or if you would like to share how insights-driven decision making helped your organization work through a critical period, please write to us at [email protected] and [email protected].

Data Literacy: An Idea Whose Time Has Come | Blog

The COVID-19 pandemic has accelerated organizations’ move from intuition-based to data-powered decision making. Why? Primarily because the sudden impact of the pandemic exposed black spots in global supply chains, shuffled demand patterns, and significantly impacted the travel and entertainment industries. Those organizations that had the capability to harness insights were able to respond with agility and resilience.

Moving from intuition-based to data-driven decision making

Data literacy is a key element of an organization’s ability to be insights driven. Indeed, data literacy is defined as the ability of an organization’s key decision makers to make data-driven decisions by providing them access to the tools/technologies to access/manipulate data and training them in how to know, speak, and argue with data.

Data literacy supports data/technology democratization by decentralizing data-driven decision making, empowering users to drive faster, reliable, and actionable decisions.

Data literacy remains in its infancy

Despite its transformative potential, industry commitment to, and spend on, data literacy remains low.

While the current market spend on data literacy is $530-560 million, only 10-15% goes to standalone data literacy engagements. A large percentage of data literacy initiatives are baked into broader data and analytics transformation programs, as data literacy is considered an adjunct to broader people change management processes.

Why is commitment to data literacy lacking?

While there is surely a lack of enterprise education related to data literacy, the larger challenge is for enterprises to provide the right data assets to the right people.

1. The right data – The right data refers to the relevant, high quality data assets required to perform a specific business task. These assets could be internal/external or first/third. Organizations struggle to build a consolidated view and repository of their data assets through a centralized data management and governance strategy. Moreover, developing the ability to exploit external data for added insights and decision making remains low on most organizations’ radars. As a result, enterprises’ data assets mostly exist in siloes and remain grossly underutilized. Moreover, lack of governance and processes around data quality hinder business users’ trust in insights generated with the existing data assets and technologies.

2. The right people – The right people refers to the business users who need to be empowered to make data-driven decisions. Enterprises generally struggle to find the right people who are trained to work with data and associated technologies such as AI and advanced analytics. The large demand/supply gap for key data and analytics skills further aggravates the problem. This shortage in talent supply is further exacerbated by low project readiness and poor domain understanding of the available talent pool.

Another key challenge is identity and access control to prevent data breaches and civil lawsuits. The prevalence of laws governing data – such as CCPA and GDPR – further impede enterprises from going all-in on insights driven decision making.

For enterprises to make data literacy actionable, they need to connect the right data to the right people supported by strong leadership commitment and a centralized governance structure.

Rewards associated with data literacy

Both IT service providers and technology companies have started to offer credible data literacy services, including both technology solutions for self-service analytics, such as Qlik and Tableau, and targeted training programs to address the demand-supply gap, like Qlik’s. IT service providers, including Accenture and TCS, recognizing the growing need for data literacy, are offering solutions that support data/technology democratization.

Data literacy used across the organization leads to insightful decision making, which helps the organization to become agile, resilient, and sustainable, increasing its competitive advantage in an aggressive and fast-changing global economy. Data literate organizations can also sustain growth during the normal evolving business landscape, as well as during black swan events such as a pandemic.

This blog is the first in a series exploring the core concept of data literacy and its importance for enterprises. If you have any questions about how data literacy can help your organization tackle complex situations, or if you would like to share how insights-driven decision making helped your organization work through a critical period, please write to us at [email protected] and [email protected].