Question

Pawan Gupta was worried. He had just been appointed the head of the knowledge management (KM) team at a leading analytics firm, Insights Global Analytics, in May 2019. Before he became head of the KM team, Gupta led the global-level corporate KM initiative at a leading knowledge process outsourcing service provider. Before that, he was a KM technologist at a global management consulting firm, where he developed Web-based knowledge maps, search tools, and custom KM tools. Insights Global Analytics handled many internal analytics processes and projects. Most of its projects required extensive domain and statistical expertise to provide meaningful insights to clients. Employees with prior analytics experience had skill-sets, techniques and heuristics that could be utilized for other projects. Likewise, analysts and consultants working on business research projects had strong domain knowledge about the various technological trends acquired over a long time. They could decipher the story and signals behind the numbers stored in various databases. However, sometimes one team did not know about the rich skills possessed by another team, thus forcing them to rely on a less-than-optimal skill set.
To address this issue, the top leadership team envisaged creating a KM platform that could be used to promote a knowledge-sharing culture within Insights Global Analytics. However, the leadership team was not sure about the technological options that could achieve this objective. Different technological options had different functionalities, benefits and costs of ownership. Gupta’s main challenge was to select technological options that would help to create a cost-effective and successful KM platform.

Accordingly, Gupta began establishing a KM platform for Insights Global Analytics. He gave himself three months to assess the various technological options and then present his assessment to the top leadership team. With the team’s approval, Gupta would establish an integrated KM, information and communication program. The program would be limited to a few teams initially before it was extended to the entire organization. Later, computers and tablets division, printers division, corporate marketing and data center business by analyzing and interpreting organizational data to facilitate data-driven decision making. It was the analytics unit of one of the world’s largest technology companies by revenue, and was among the world’s top 50 valuable brands. Insights Global Analytics had 700 employees, mostly PhDs, MBAs, chartered accountants and statisticians from premier educational institutes in India and overseas for solving problems related to business decisions, planning, business intelligence optimization, supply chain planning, Web analytics and marketing strategy support.
The success of any analytics project was dependent on providing quality insights based on the data analyzed. Depending on the complexity of a business question, teams worked together to integrate statistical and business knowledge and to deliver meaningful insights. The top leadership of Insights Global Analytics, being an internal analytics unit in the knowledge-intensive sector, knew that it had the employees and knowledge base to stay ahead of stiff competition from alternatives such as third-party vendors that might handle the outsourced analytics work; however, the company lacked an effective avenue for sharing knowledge across teams. Without a platform for sharing, employees faced difficulty in identifying which teams or individuals could help them.
Insights Global Analytics also handled many processes using data to provide regular insights into markets, products and business operations. Employees involved in the processes had

developed strong domain-specific knowledge and skills, such as automation to: reduce turnaround time, minimize errors in data analysis and reporting, and improve productivity; however, when they transitioned to new roles, the company often lost the employees’ automation and domain-specific knowledge crucial to interpreting data and to employees working on other teams. Daily operations showed the need for a platform for sharing knowledge.
Insights Global Analytics extensively used statistical tools such as Excel, JMP and SAS, and statistical techniques such as market-basket analysis and time-series analysis. As the use of advanced statistical tools and techniques was rarely taught in schools, many of the analysts who joined Insights Global Analytics were interested in learning these advanced tools. As such, top management felt that a KM program was useful as a platform for employees because it would allow them to post their learning queries to the statistical experts in the unit more efficiently.
Gupta came to Insights Global Analytics with a mandate to initiate a KM program platform that would facilitate the sharing and documentation of organization-wide knowledge. He realized that the market had abundant KM tools to use for documentation but the success of the KM program depended on whether employees perceived the knowledge sharing as useful — and even fun — rather than as an additional burden. Gupta favored using unconventional approaches to KM implementation to include abundant tacit knowledge pertaining to analytics techniques used for different processes and projects. In addition, conventional approaches would encounter difficulty in documenting many of the heuristics involved in analytics procedures. The Insights Global Analytics workforce was highly skilled in terms of educational qualifications and domain knowledge. If the KM program solely focused on documenting the underlying knowledge, it would use a technical jargon familiar to specific domain specialists only.

Employees who worked in other domains or who had other skill-sets would find the program incomprehensible, so its utility would be restricted to team boundaries. Hence, the KM platform would fail to achieve the primary purpose of enabling knowledge sharing across teams.
Gupta’s major challenge was to select cost-effective technologies that would facilitate and promote knowledge sharing. He worked with Arun Sharma, a technical leader who had experience in Microsoft SharePoint, wikis, blogs and content management. Aware that Insights Global Analytics had high expectations from the KM program, Gupta and Sharma pondered their various technological options. With a few members of the leadership team and middle management, they brainstormed and identified three broad options: (1) technologies already used in the organization; (2) open source solutions; and (3) paid KM solutions. But at the same time, both of them somehow feel that we have reduced the KM issue to a mere technical issue. We are only discussing the functionalities of platforms and their cost of ownership. We are confused about how these technological options by themselves will encourage the sharing of knowledge. We must think beyond the platform and consider a mix of options and initiatives that will foster a knowledge-sharing culture.

3. Do you think Insights Global Analytics knows professional usage of statistical and data management tools? Justify. (CLO#6)
4. What did Gupta and Arun Sharma identified regarding KM techniques after brainstorming with members of leadership? (CLO#6)
5. Overall, what did you learn from this case study based on Knowledge Management techniques? (CLO#6)

Answer 1

Q 1. Do you think Insights Global Analytics knows professional usage of statistical and data management tools? Justify.

A 1. Yes, Insights Global Analytics knows the professional usage of statistical and data management tools. The reasons for this are

• Insights Global Analytics is a leading analytics firm and to be one the leading analytics firms one needs to have good knowledge of professional usage of statistical and data management tools
• Insights Global Analytics was the analytics unit of one of the world’s largest technology companies by revenue and was among the world’s top 50 valuable brands.
• Insights Global Analytics had 700 employees, mostly PhDs, MBAs, chartered accountants and statisticians from premier educational institutes in India and overseas for solving problems related to business decisions, planning, business intelligence optimization, supply chain planning, Web analytics, and marketing strategy support.
• Insights Global Analytics extensively used statistical tools such as Excel, JMP and SAS, and statistical techniques such as market-basket analysis and time-series analysis.

Q 2. What did Gupta and Arun Sharma identified regarding KM techniques after brainstorming with members of leadership?

A 2. They identified three broad options:

1. Technologies already used in the organization
2. Open source solutions
3. Paid KM solutions

However, both of them also realized that they had reduced the KM issue to a mere technical issue. They were only discussing the functionalities of platforms and their cost of ownership. They realized that finding technological options was a small part of the solution, the major challenge was to encourage the sharing of knowledge. For this, they had to think beyond the platform and consider a mix of options and initiatives that will foster a knowledge-sharing culture.

Q 3. Overall, what did you learn from this case study based on Knowledge Management techniques?

A 3. Know Management techniques have 2 aspects, first, one being the technology/ platform on which the knowledge is shared and managed and the second is the culture of utilizing this platform. There will be people who will be willing to share knowledge, however, the sharing has to be in a medium that is understandable by all and not be filled with technical jargon. The culture that is to be developed for knowledge management should have traits of learning and sharing. This focus on both the technology and culture will result in successful knowledge management which in turn will result in a successful organization.