Due to the rapid pace of adoption, the global analytics market is set to rise from USD 28.12 billion in 2021 to USD 118.42 billion by 2028. Many enterprises are building meaningful partnerships with analytics outsourcing providers, while some are updating their existing analytics infrastructure, with a host of organizations just getting started on their analytics journey.
Building an analytics team that can address and manage diverse business expectations is not an easy task.
Some questions we often get from business stakeholders are:
Essentially, entrepreneurs want to know how to build a high-performance analytics team at every step of the organization’s data maturity journey. The answer to this question would vary depending on the type of organization involved. You may be small to a medium enterprise looking to invest in a dedicated analytics team, or a multinational corporation seeking to enhance your analytics capabilities. However, the essential recipe for success remains the same for all.
This article is aimed at understanding how you can set up your analytics team structure for seamless deployment and efficient operation. But first, let’s take a quick look at the fundamentals of business analytics.
Business analytics is a multi-faceted process, demanding an amalgamation of expertise from mathematics and software engineering, to data discovery and data visualization. It involves the application of business intelligence tools for analyzing various aspects of a company, ranging from the performance of specific business functions, products, and services, to customer experience and brand equity.
Your business analytics framework is heavily dependent on the industry you are involved in, your business structure, and what you expect from your analytics investments. For example, if you are in consumer goods manufacturing, analytics can help you in a wide variety of ways from optimizing your manufacturing process, to improving product development and innovation. If you are directly involved in the retail industry, employing analytics can help you track customer behavior across channels, predict market trends, and so on. Insights from these analytics will in turn enable you to manage inventory, customize marketing strategies, influence sales, and boost ROI.
Building an analytics team, comprising state-of-the-art BI tools being operated by skilled personnel, can allow you to transform your business from the ground up. The following are some considerations to be made while constructing your analytics team structure.
There are a few fundamental ideas that should guide you toward successfully setting up your data team.
The following are three factors you need to consider when building your analytics team – the size of the team you wish to set up, how centralized you want the team to be, and how it integrates with your company’s overall data strategy.
Building an analytics team will not involve the same components at every organization, but you can’t expect high performance with just a group of Business Analysts. The different roles comprising the team can be broadly categorized into leadership, business, and technical roles. Each of these roles has individual functions within them, where personnel are proficient in their respective set of capabilities.
A well-constructed analytics team should ideally include the following personas:
First, you’ll need someone to head your analytics initiatives. It is the Chief Data Officer’s or Chief Analytics Officer’s job to set an analytics vision for the business. This person will need to ensure that analytics is a well-respected function with a strategic voice and ongoing participation in execution in the context of organizational objectives. So, make sure this person is ready to take on internal business leaders and external leaders if required before setting up the rest of the team.
Personnel in leadership roles should also have strong skills when it comes to communication, motivation, delegation, conflict resolution, and problem-solving, apart from the requisite analytical capabilities.
These are Systems Architects, Data Engineers, Data Scientists, Data Architects, and related personnel, who are the masters of data discovery, extraction, and governance. They aren’t the developers of the BI tools themselves, but the human intelligence required to deal with the logistics surrounding the automated analytics process, including source data governance, acquisition, management, aggregation, security, and scalability.
You need a strong technical team to adequately support your BI team and its analytics requirements.
They will be the first layer of validation experts, ensuring data gets populated in the right cadence, frequency, and quality. This team is also responsible for streamlining server architecture for on-premise solutions or managing administrative capabilities for cloud-based BI solutions. They know how to establish a flawless data foundation, and have a keen eye for creating the best possible development environment.
These team members can help with requirements gathering and project management for analytics projects, produce static and dynamic BI, third-party in-tool reporting, and base-level analysis. They are your foundation for establishing subject matter expertise on the team, and your first tier of support for analytics requests. Value and nurture them as much as possible.
Members of this team also need to possess communication and collaboration skills, apart from their capabilities for analytics and data interpretation.
Builders of reporting apps and BI solutions create easy-to-use applications for the BI and business teams. As the skillsets of data providers and the BI teams vary, app builders help bridge this gap.
We miss out on this aspect more often than not. The storytellers are the people who identify the narratives behind the data, helping you visualize the right insights through your dashboard. They add context to the data, ensure accurate delivery, and leverage their UI-UX skills to help you comprehend the business intelligence from the raw data.
While developing your analytics ecosystem you need to identify which of the above-mentioned analytics team roles your data team really requires. This will also help you map out if any of the personnel will be capable of contributing across functions and projects. Data analysts and architects can leverage their capabilities in varied projects, making them flexible assets within the team.
Onboard the right talent, so that your data team is not just able to handle the projects assigned to them, but can operate smoothly. Every individual working in coordination with the rest of the team will generate positively influence productivity, and foster a great work environment.
Building an analytics team requires payroll setup, technology and hardware investments, and initial seed money. All this requires investment and the two following approaches could work here.
The data team needs to work closely and collaborate with engineers, product managers, product designers, marketing teams, and sales personnel. Analytics impacts every business function, and real-time insights can revolutionize the way that each function operates, and the decisions that they make. Effective collaboration across functions can help you enhance the entire organization’s operations from the ground up.
Organizations today have data amalgamation and reporting processes that typically run in silos. The larger the organization, the bigger the silos. And that is why the role of the internal analytics team becomes so important—after a few pilots run, their role is to champion the cause of analytics across teams from time to time. Eventually, once the team matures, they can define future analytics roadmaps in collaboration with the business teams.
Managed services are heavily process-dependent and come into play when there is an unexpected gap in the team, maybe due to a member leaving the organization. It ensures that there is business continuity and provisions in place for dealing with such situations. Internal or external disruptions should not impede analytics initiatives, and considerations have to be made for accommodating contingencies.
With the introduction of new technology, there has been an increased ability to compute terabytes of data at a rapid pace, along with beautiful visuals created by reporting and analytics tools such as Tableau and PowerBI, helping with data visualization and understanding business insights.
It is imperative to train your analysts and other functional folks in new technology and methodologies. This is even more essential in the case of advanced data science personnel, who work in a field where there is a constant evolution.
As business owners, we want data and analysis to be available yesterday. However, a lot of data processing needs to happen before we can see the dashboard. Hence it is imperative to set expectations for the delivery of the insights and data among the business stakeholders. The timeline for data being converted to BI should be clearly defined, or else it does not take long for small issues to snowball into larger problems.
Building an analytics team within the organizational context is a cultural change, and needs to be handled appropriately. As you onboard and start building internal teams, there will be reluctance from old-timers and existing practitioners resisting change, so it is important to communicate the value that will be added by the new teams, and ensure ongoing communication across teams is not affected.
Here are a few guidelines that can help ease the transition:
Once the team is operational you can also accentuate your analytics initiatives, by accessing expertise from an external source. Analytics Outsourcing can enable you to support your in-house team with the exact industry expertise that might be needed for a project. Your analytics team structure can also gain some flexibility, with an additional helping hand working in cooperation, and delivering insights. Assess your situation, make the right decision, and optimize your analytics journey for maximum potential.
Note: This article was recently updated to offer deeper insights into business analytics and building an analytics team.
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