When and How to Build out Your Data Science Team
Increasingly, startups across the spectrum are looking to Artificial Intelligence (AI) to help them solve business problems and drive efficiency. The numerous benefits of building AI capability in your startup shouldnʼt come as a surprise to anyone.
In fact, the advantages for business are so far-reaching that PwC predicts that AI will add $15.7 trillion to the global economy by 2030. Contrary to popular belief, successfully implementing AI to drive impactful decisions requires a diverse team with expertise in several skill sets.
Launching your AI journey is no simple feat — you need to ask probing questions to ensure that the relevant data science projects are embarked upon at the right time. Plus, you need to make sure that you build out an effective team that can turn data into decisions.
Most startups are already using data to understand their business performance and make operational decisions, be it through MIS reports or KPIs tracked on Excel spreadsheets. But when should you level up to advanced analytics? At what point should you start leveraging AI technologies and build out your data science team?
Any startup looking to mature into data science needs to have access to credible data sources and clean data. So, a prerequisite is to leverage engineering practices to source data, then structure and store it.
The first step in data science is looking at descriptive analytics to understand what happened in the past — this is usually achieved through KPI reports and simple summaries of business metrics.
After conducting descriptive analytics, you should then use exploratory data analysis to understand why things panned out the way they did. This is known as diagnostic analytics and is powered by statistics and business analysis.
Once you find yourself seeking to extract more value from your data, you should plan for forward-looking analyses. Here, using AI and machine learning (ML) is vital to dive into deeper, predictive, data-driven insights.
Next along the data science journey is the consumption of these data insights and connecting them to business decisions. Data consumption is powered by narratives, information design, and data stories.
The final data maturity level at an organization is when data becomes a culture. This is where it is second nature for everyone in the organization to use data and leverage techniques like exploratory insights, ML and AI, either directly or indirectly to power decision-making.
But what about startups just starting out? Should you spend six months building out your data lakes before getting into insights? No. Itʼs never too early to start training AI and ML algorithms with whatever data you have at hand — just make sure you have the relevant, curated data from internal or external sources.
Early on in your journey, itʼs vital to put in place processes to clean and transform the data before you can start feeding it into your data science layer. For example, letʼs say a recently-launched e-commerce startup wants to leverage AI and ML to better understand its audienceʼs buying behavior. The data science team can collect the data from transaction logs, process it, and clean it.
They can analyze this data to then generate insights on why people buy certain items and predict what they are likely to buy in the future. This provides the organization with key actionable insights into their consumer base early on rather than having to wait.
So, youʼve made the decision to take the leap into AI, and youʼve identified your AI vision and the likely use cases to get started with. How can you then build out your team to realize the benefits and scale the stages of maturity?
If youʼre starting at the point of reporting and descriptive analytics — then youʼre going to need a data translator. These folks are closest in skill to ‘business analystsʼ in Business Intelligence (BI) teams.
