How to Create an Effective Data Analytics Team? Explore Key Processes Involved
“TEAM Stands for T – Together, E – Everyone, A – Achieve, M – More”
The world today is driven by data, organisations are dependent on the huge bulk of data that is being collected from disparate sources. Capitalizing on data is vital to business success and hence data analytics is now an integral part of any business. Performing effective and robust data analytics depends on creating the right data analytics team.
Demand for data analytics professionals has jumped a big margin over these years. Organizations have started to understand the significance of having a perfect data analytics team that recognizes the intricacies of data and its associated components.
Creating a perfect team for data analytics is a tough task. It is not as simple as merely hiring good resources. There are many other factors that must be analyzed and worked upon to ensure optimal usage of resources, technologies, and infrastructure.
How to create a good data analytics team?
What are the key contributing factors to that?
What are the best practices and key tips that can help in building a data analytics team that can offer wonderful results?
This article attempts to answer all these questions by showcasing the important parameters that are needed to build an effective data analytics team. Prior to that, let us have a fleeting look at what data analytics is.
Data Analytics – An Overview
Data analytics is the modern-day science of analyzing raw data for determining conclusions and actionable insights. It uses tools, technologies, and a best-fit data analytics team to extract unseen trends, and hidden patterns, discover correlations, and resolve issues. It is an iterative process and data analytics teams must be able to adapt themselves to the functioning of the process. They must also be capable of making changes such that overall productivity and profitability increase.
Data analytics examines and analyses large data sets to get insightful information that can reform business processes, enhance decision making and increase business growth. It collects, transforms, and organizes data to make predictions, draw conclusions and take informed decisions.
Who Should Be There in A Data Analytics Team?
Building a creative data analytics team is like investing more in people resources rather than tools and technologies. This is extremely important. Here are the key players in any data analytics team that play important roles in its functioning:
- Data Scientists – Handling machine learning models and complicated algorithms with data-driven decision making
- Data Engineers – Offering technology and infrastructure support to other teams with proper receipt, transformation, and access to data
- Data Analysts – Managing data cleansing and transformation activities for the entire chunk of data
- MLOps Team – Helping in implementing machine learning initiatives and enhancing business value
- DataOps Team – Taking care of databases, data stores, and transformation pipelines to ensure secure data quality
- Business Analysts – Collaborating between the technical teams and business teams to ensure the integration of work
How to Create an Ideal Data Analytics Team?
To have an ideal data analytics team, there are certain processes and best practices that must be done, in an effective manner. There is no fixed procedure to have a good team but abiding by the below-mentioned activities can surely offer a rewarding team strength:
- Decide Your Operating Model
Your data and analytics operating model is the main decision that helps you reach your objectives and gain maximum value. The models depend upon the type of environment and place where the teams will reside.
The different models could be a decentralized operating model – distribution of responsibility across other lines of business, centralized operating model – a structured way of responsibility falling under a specified function, hybrid operating model – the best of both worlds and data labs – for setting up in-house teams with flexibility and agility.
- Work on Defining Precise Goals and Prospects
It is important to understand and create perfect goals for the data analytics team. The better the goals are, the more accurate will be the product. The defined objectives will encourage the team members to function on a productive level, increasing team efficiency and garnering meaningful output. It must be ensured that all the members of the team must be updated on the goals and prospects so that their thought process works on a symmetric level and brings about a systematic output.
- Establish a Strong Data Foundation
As you talk about growing business, the data analytics team must seep in a strong data foundation with a robust data culture. A central data culture can bring about a cohesive environment where all teams work in sync with each other. There is a common understanding between the teams, leading to optimal output from each other. The entire experience becomes very interactive, engaging, and useful.
- Create an Interface Between IT Teams and Business Teams
Two critical groups that work towards accomplishing a software project are the IT team and the business team. The data analytics team must be grouped in such a way that it acts as a bridge between the two teams. They must create a positive environment that can bring on innovative ideas and collaborate well with each other, bringing in value addition for the business.
- Consider Each Phase of the Data Lifespan
Data management and analysis is a whole big process that encompasses many functions. While creating the data analytics team and making it functional, it must be ensured that it covers the entire lifespan of the data lifecycle. Else there are chances of leaving behind opportunities for data integration, enhancement, etc. To get optimal value out of data, the team must take into consideration the entire life cycle.
- Define Roles, Responsibilities, and Functions of Involved Stakeholders
As we create the data analytics team, the composition of the team must equally comprise all the different roles, as mentioned above. There must be well-thought-of distribution of resources, roles, and responsibilities so that each can carry their own functions with ease. The defined functions can help in data preparation, integration, transformation, and management.
- Lessen the Gap with External Technical Specialists
Due to expert advice needed by the data analytics team, there are occurrences when the team must deal with external specialists for certain requirements. Teams may have to alter their structure for better support, efficacy, and scalability. These external partners are well proficient with different industry segments and hence can offer optimal advice. This ensures increased collaboration and ties up all the working units together.
- Create Genuine Footing of Data with Skilled Analysts and Engineers
Once your team is ready, have a perfectly defined data architecture for better control. This will ensure better scalability, and accurate analytics and create a detailed repository for further line of action. It will help the team in deciding the future steps and sync them in line with the team objectives. The clear and well-defined approach will help them all in setting up a robust environment for the entire analytics procedure.
- Gain Confidence by Showcasing Smaller Chunks of Information
What you see is what you believe. So, to increase the trust factor, it is advisable to work on the organization’s KPI and start creating the basic dashboards and reports that can map the KPIs to the data. These KPIs and reports will provide insight into the data helping in the future evaluation of business decisions.
The data analytics team is an indispensable ingredient of any modern-day organization. Having qualified talent on board with the best of team arrangement can work wonders in taking the right business decisions and enhancing customer satisfaction levels, by a great margin.
Organizations must engage in creating detailed hiring plans and offer attractive packages to attract the right kind of talent. They must create data foundations and build a strong team with multi-faceted skills and a robust delivery value.
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