Data Culture and Language of Data in Workplace

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As a part of theme of organizational culture, we spoke to Manoj Kumar about the data culture and the language of data in a digital organization and sharing some excerpts of our discussion.

Q- What is the language of data? Why should that be part of the organization culture?

In the digital economy, AI first is the tagline for most of the organization to remain relevant in the future. There are 2.5 quintillion bytes of data created each day, but the pace is accelerating fast as mobile, social media, e-communication, digital photos, IoT, and platform-driven daily-life services are turning quickly into a way of living in our society. Data itself is useless unless processed into information and narrated in the business language. AI-driven technologies are helping organizations make sense from this data explosion.  However, the challenge is that not many organization speak information fluently across all the functions. Microstrategy’s 2018 Global state of enterprise analytics report states that 71% of enterprises expect to accelerate their investment in data and analytics in the next three years. On the other side, Gartner analysis ranks data literacy as the 2nd most roadblock for the successful adoption of data and analytics. Organizations need to master this capability and embed into their organizational culture to realize the return from their organization-wide interconnected data and analytics assets.

QWhat are the benefits of speaking data language?

There are many, but the top five benefits are:

  • Faster and better business decision-making more often.
  • Improved collaboration across functions as this becomes a universal language across the organization.
  • A better understanding of the current data landscape within the organization which helps in building future data collection plans too.
  • Leads to improvised data privacy, control, and governance 5- helps to democratize the data in the organization. 
  • Helps to democratize the data in the organization.
  •  

Q- What are the essential elements of speaking information and how to master them?

Learning to speak information is very much similar to learning any other language – vocabulary, grammar, and context. Similarly, the information language too consists of five elements, i.e., data elements and data-sources understanding, techniques applied to process the data, intuitive visual to structure the output in the appropriate business context, and narrating a compelling story. These five elements can further be categorized in three as inputs, process (analytics), and outputs (business values) – adopted from Gartner (see pic 1). It can act as a common language across all the creators and consumers of the data in the organization if embedded well. 


Picture 1 (adopted from Gartner’s ISL model)

Q- Can you explain these elements with an example?

Sure. Let’s take an example from HR data and analytics to explain the concept further.

Business value as Output: Understand the persona of top performers in the sales organization to enhance salesforce talent recruitment and learning & development framework.  HR Analytics shows that top-performing salespeople spend 30% more time with customers, maintain a 25% larger network in the organization in and across relevant functions, and are engaged 25% more with management compared to others.
Data, sources, definition, and privacy as inputs: For the use-case mentioned above, employee basic demographics (no personal identifiers),

communication interactions, frequency, sources of the data elements, and the data labels classification are the inputs. The understanding of the data elements and associated privacy levels are essential understanding before the data is converted into a wealth of information.

Analytics techniques as the process: Once the inputs are understood clearly, Organizational network analysis (aka social network analysis) is used to analyze the communication pattern, identify drivers, broken links, and strength of the relationships. Then after, a narrative is drafted keeping the business context (as articulated above) at the center.

Q- How to embed the information language in the organizational digital culture?

There are three critical influencers in creating a data information culture in the organization, i.e., creators, consumers, and sponsors of the data. All the three parties should remain informed and invested throughout the journey of the program. Developing data-information culture requires a continuous change program. Still, companies can get started on their own by doing few things in-house.

  • The organization can conduct an assessment first to benchmark current capability. The study should include people, process, data, infrastructure, and culture for the completeness. The findings will allow companies to mark the gaps and plan way-forward strategy to bridge them.
  • To democratize the data across the organization, you need to make it available at the grass root level. A right IT infrastructure could be a good enabler to fast-forward the journey. Put a common language (data definition) for consumers and creators so that they treat it in a standard manner.
  • Data Literacy Platform: Providing just data or insights will not suffice the need unless consumers will speak the language in the context of the business. They say if you want to learn a language, go to where that language is spoken. Create a platform in collaboration with Data Analytics and industry where people can interact, converse, and learn the data language.
  • “The” myth: Data culture is meant mostly for the business lines and few of the functions like marketing, risk, and finance. The data-information culture program is intended to be organization-wide else will not deliver the expected return. Break the data and information silos.

Q- How Workplaceif is helping the industries get better on data literacy?

To bring this change in a sustainable manner, we need to work with two segments of consumers/creators – one who is already in the corporate world and second, who are about to start their career. To cater to both audiences, we have taken a two prolonged approach:

  1. We are working in collaboration with the management institute to elevate the knowledge of data, analytics, and language among students, alumni, and faculty members. Our programs are custom-build and industry-focused to suits the audience. We intend to help our future talent be ready with this must-have competency before they leap onto the corporate path.
  2. We are working with clients on this journey end-to-end, starting from conducting the assessment, drafting the blueprint, delivering interventions to strengthen their people, process, data, infrastructure, and culture so that it gets engrained in the organization DNA.

Additionally, we try to disseminate the knowledge through conferences, newsletters, research, and articles in the industry magazines. 

Q- Any last few words for our readers.

The current digital era, we are experiencing a shift where organizations are expected to offer better and faster services to their customers and employees more often. It is believed that interconnected breadth of data and analytics will provide not only such customize services but also a source of competitive advantage.  This competency must be embedded in the organizational culture to realize the full potential of data and analytics value-chain.

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