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Building the High Performing Team for Enterprise Data Analytics

Building the High Performing Team for Enterprise Data Analytics

Prashanth Southekal and Santhosh Raju

Introduction

High performing teams hold the key for the successful performance of any company. Whether you have thousands of employees or just five employees, high performing teams are a must for optimal business performance. Successful analytics initiatives are no exception and are also dependent on high performing teams. However, most Data Analytics teams today are a shadow of the old MIS/BI team structure and typically reporting into the CFO function. These teams are organized around the specific IT skills that is often a combination of ETL (Extract-Transform-Loading) Developers who build and maintain a data-marts and data-warehouses, Business analysts who capture the needs of business users for operational and BI reports, and report builders who run queries and build reports.  In addition, the most of the current Data Analytics teams have been reporting to the Finance function which is typically a cost controlling and regulatory function in most companies and typically averse to innovation and change. What is needed in the new environment where every company is a data company is an operating model that  focuses on innovation, scale & value creation. This environment in-turn will empower knowledge workers in the business with right tools and technologies to achieve their objectives.

 

Building the Analytics Team

Unfortunately, there is no one standard way to build a high performing analytics team. Building a strong analytics team varies from one organization to another and is usually contextual. Some of the characteristics of effective teams like communication, trust, a clear sense of purpose, supporting each other, and so on are applicable to Data Analytics teams as well. But there are some characteristics which are specific to Analytics teams. Fundamentally, Analytics teams of today demand a structure with a solid understanding of the business, the data driven process and the decision-making mechanism in the organization. In this backdrop, below are five key elements or characteristics that are specific for high performing Enterprise Data Analytics teams. 

 

1. Data Literacy as the Foundation

The success of the Enterprise Data Analytics team rests heavily on establishing a culture of Data Literacy in the entire organization. Essentially Data Literacy can be achieved by creating an environment where everyone in the organization uses insights from data over intuition to augment their decision-making process. Data Literacy can be achieved with senior management support by building awareness on the business opportunities lost and the compliance risk that exist due to lack of Data Analytics Products and solutions in the organization.  In other words, in the organization structure, the Analytics teams should sit closer to the revenue generation functions like Sales, Operations or Innovation as the needs from these teams is inherently value creation and revenue generation with focus on customers and not sustainment or compliance. 

 

2. A Strong Analytics Leader

Once the culture of Data Literacy is formulated, we need a strong leader to run with it. The Analytics team should be led by one who has a solid understanding of data, technology and the business so as to translate the vision and the needs of the business stakeholders into a clear strategy with measurable results. Mckinsey Consulting calls the Analytics leader as a Catalyst—who embraces a style of leadership addressing the current demands and roadblocks and deploys Analytics solutions at scale. Enterprise Data Analytics today is basically the new language of business communication. The Analytics leader should have the skills and experience not only to build the culture of Data Literacy, but also to educate and drive implementation of insights throughout the organization.

 

3. Staffing the team across the entire DLC

As said before, traditional Enterprise Data Analytics team focused on technical capabilities like ETL (Extract-Transform-Loading data) and Report building. However, the current need from the Analytics team is to build a multi-disciplinary Analytics team across the entire data lifecycle (DLC) especially on Data Capture, Data Engineering, Data Science and Data Visualization with support from the Data governance team. The team members from these diverse groups should integrate seamlessly with business stakeholders by talking the language of business and data more than technology.

4. Hypothesis-based Methodology

Once we have a Data culture, leader and team established, there has to be a methodology for delivering Analytics solutions at scale. A hypothesis-based methodology will shift the Analytics solution implementation methodology from projects to products. A hypothesis is basically an educated guess at explaining. In this backdrop, a hypothesis-based thinking will offer the Analytics team an early and quick insights and sets the stage for an iterative and incremental analytics process. While there are many techniques for developing hypothesis-based thinking, one key technique is the McKinsey’s thought process called MECE - an acronym for Mutually Exclusive, Collectively Exhaustive, separates the problem into distinct, non-overlapping issues while making sure that no issues relevant to the problem has been overlooked.

 

5. Execution Mechanism

So, with the right philosophy, organizational structure and mandate, how will the Analytics team execute? There are 3 key elements in delivering or executing Enterprise Data Analytics solutions. Firstly, Analytics team should work closely with business stakeholders who believe in leveraging data for business performance. Secondly, Analytics team should start small and focus on building trust and credibility with the business. Small could be a small number of use cases, engaging a small number of business stakeholders, working on a small data sets with sample data and smaller time frames, small budgets, smaller projects like proof-of-concepts (PoC) and so on. Thirdly focus on “good enough” analytics solutions. Analytics initiatives will rarely be fulfilling all the needs of the stakeholders when they are first deployed for numerous challenges. Analytics solutions take refinement  in an iterative and incremental manner. As they say - perfection is the opposite of getting things done. Analytics teams should work on showing some small and significant wins quickly so that they can be positioned for bigger success in the Enterprise. 

 

Conclusion

Today building high performing Enterprise Data Analytics teams is more than staffing people with ETL and Report building skills to focus on compliance and historical performance reporting. Enterprise Data Analytics today is a value creation function and the new language of business communication if an enterprise is serious on leveraging data for improved business value. In this regard, the Analytics teams should work on ensuring that the data and insights are in the hands of managers and front-line workers who will regularly use data to drive better business results.

 

About the Authors

 

Prashanth Southekal is the Managing Principal of DBP-Institute, an Enterprise Data Analytics consulting and training firm. He brings over 20 years of Data Analytics experience from companies such as SAP AG, Shell, Apple, P&G, and General Electric. He is the adjunct faculty of Data Analytics at University of Calgary (Canada) and IE Business School (Spain). Mr. Southekal is the author of the book - Data for Business Performance (DBP) and he is currently working on his next book on - 10 Key Enterprise Analytics Best Practices for Business Results. Prashanth lives in Calgary, Canada. For more on Prashanth, visit - https://www.linkedin.com/in/prashanthsouthekal/

 

Santosh Raju is an experienced consulting practice leader with over 20 years of experience providing data, connected and digital solutions. He brings extensive experience providing innovative solutions across various industry verticals, growing and shaping the BI/IM/AI/ML/Big Data practices for improved business results. He is a speaker at several industry events and advisor to several start-ups. Santosh lives in London, United Kingdom (UK). For more on Santosh, visit https://www.linkedin.com/in/rajus/

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