Question

CASE STUDY# 2:
INSIGHTS ANALYTICS: TECHNOLOGY FOR A KNOWLEDGE MANAGEMENT PROGRAM.
What did Gupta and Arun Sharma identified regarding KM techniques after
brainstorming with members of leadership? (CLO#6)

CASE STUDY# 2:
INSIGHTS ANALYTICS: TECHNOLOGY FOR A KNOWLEDGE
MANAGEMENT PROGRAM
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 haddeveloped 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 onlyEmployees 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.

Answer 1

Main challenge that Gupta faced was to select technological options that would help to create a cost-effective and successful KM platform that is interactive and just not technology-based. He along with Arun identified that KM Platform should also be a fun and learn kind of platform and not a liability or forced approach to the wide talent pool the company has. By making it too technical or system-based, it would not draw the attention of employees to learn something which is not their forte/domain or else would limit the knowledge management to those who can comprehend it. In other words, making it a share on a technical platform to someone who does not have the subject knowledge or domain knowledge, the postings would still remain incomprehensible and hence the basic purpose of launching a KM initiative would be lost along with interest of various teams having their own skillsets varied from the other.

Gupta wants that the platform should be fairly interactive, should be comprehensive by those who have no clue about the subject or domain, should facilitate sharing knowledge and adding skills to different teams at the same time. For this reason, Arun came up with making it a three-level KM platform:

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

With these three levels, he anticipated that employees have all options to select from. Those who want can receive knowledge from open source solutions and the existing technologies used in org. While those who want to upgrade their skills can even go for a paid KM solution.

However the major point which was the initial agenda; that is, using the skillset of other teams who has more knowledge on the requirement than existing-working team or are better placed to address the project remains unserved since the knowledge and skillsets available in talent pool is unknown to each other. For eg. if A knows that B has a good hold on automation and B knows that A has a good hold on strategising, they can leverage their skills with each other as and when required. But if they don't, then they will only find sub-optimal solutions. The problem with Insight Analytics is just the same. Inspite of having a wide and varied talent pool, it is unseen and unknown to different teams and hence solutions which can be great are just optimal or sub-optimal.

making a platform with these levels of sharing and upgrading limits it to technical systems again where people might not be landing always making the basic purpose of Insight Analytics unserved.