Would you like to?


Six key ingredients for a successful analytics delivery team

Posted by: 
 | Sep 2018

Many organisations see value in data but are not able to tap into its true potential because they either do not know how to go about setting up the analytics team or the analytics team has not been successful in driving a difference at scale.

From our experience supporting clients across various industries to maximise data-driven insights for positive business outcomes, we have seen that it’s not just maths and data science skills that form the foundation of an effective analytics team. Rather it requires an ensemble of skills to deliver end to end solutions, that in turn drive profitable decision making.

Here we share six essential elements for a successful analytics team, whether purely in-house or working with external partners.

  1. Business partnership and communication management – From defining the problem upfront, to deployment, and finally realising commercial benefits, the entire journey requires deep understanding of business processes and how the solution will be used. During the process, maintaining stakeholder relationships through timely engagements, and ensuring feedback loops and review points with the users, are key underlying success factors.
  2. Data knowledge As we know, ‘Data is the new oil’ and hence data knowledge remains pivotal. A team should have data experts or data scientists/ analysts who can develop a thorough understanding of data sources, mine for patterns, create analytical models with relevant features, and report on business-driven KPIs.
  3. Data engineering With the growth in data, both structured and unstructured, organisations are using traditional database such as RDBMS and big data infrastructure with varying levels of information security, while facing the challenge of harnessing data stored in multiple locations – on-premise on various servers, in the cloud, or a combination of both. To add to the complexity, many organisations have heaps of streaming data from sensors, videos, websites and social media channels, making accessing data and working with databases a complex process. Having data engineering skills in the team helps with laying the right data pipelines for use by analysts, integration with analytical output, supporting real-time apps and creating the all-important visualisation layer.
  4. Core data science or analytical skills These skills comprise a unique blend of mathematical, programming/IT, and business capabilities. The team should be able to perform across the analytics maturity chain – from descriptive and diagnostic, to predictive and prescriptive. Varying levels of knowledge on statistics and machine learning are required, depending on the problem the team is trying to solve and the desired insights and outcomes.
  5. Solution deployment Business stakeholders have little time to wade through reams of raw data, so it is important to translate complex technical outputs into an intuitive interface. Solution architects and tech design play an important role in deploying the solution with the required access layers in different phases e.g. proof of value, prototype, or production environment.
  6. Project management Governance and project management are critical in ensuring development is happening on schedule and moving in the right direction. These functions enable all parts of the team to be effective in terms of delivery milestones, tracking burn rates of resources, highlighting risks and issues, and addressing roadblocks. Once the solution is delivered, strong project management processes enable the team to engage with business users, track adoption, seek feedback, understand benefits, calculate ROI, and communicate outcomes to leadership.


As a final tip: embedding an overarching element of coaching and development of business users drives long-term gains. Inculcate a culture of self-serve within the business and train users to be able to source the right numbers from the database or data platform, instead of taking up the skilled analytics team’s time with ad hoc data requests. At the same time for your analytics team, embed a culture and an environment that is focused on ideation, innovation, and continuous learning with a focus on enhancing value and driving results for the organisation. Leveraging the support of external partners with specific data, analytics and sector expertise can really help in this regard.


At The Smart Cube we combine advanced analytics, data science and technology to solve our customers’ most pressing problems: from bespoke solutions such as merchandising analytics and revenue growth identification, to comprehensive Analytics Centre of Excellence support.

If you’d like to learn more about how our Analytics & Digital solutions could help your business adapt to changing consumer and market dynamics, please get in touch.

  • Nisha Purswani

    An advanced analytics and consulting professional with over 10 years of experience in retail, CPG, and pharmaceuticals sectors, Nisha is responsible for managing large analytics accounts, particularly in the retail sector. She is an expert in marketing strategy, CRM, measuring promotion and campaign effectiveness, test and learn, forecasting, time series analysis, and driver analysis. When Nisha isn’t helping clients solve business problems, she can be found reading books, or in the kitchen trying out new recipes. She also enjoys travelling, meeting people of different cultures, and exploring new places.