If you ask any small and medium-sized business (SMB) about its top priority, then their answer, similar to larger companies, typically will revolve around improving customer experience, along with retaining the growing customers as well as increasing revenue. Working with data to bring actionable insights has proven to be the way forward to achieve growth and to stay competitive in the market. However, collecting and storing data is not beneficial unless it can actually drive actionable insights that propel the business forward, and to achieve that creating a data science team for your business is the most crucial step. This data science team can help the businesses in building projects that will be deployed into production for bringing value to their business.

However, to derive the maximum output from this data science team businesses must embrace the structures and resources necessary for them to thrive and generate impressive ROIs. Here are some simple steps for businesses to follow, in order to gain maximum profit out of their small data science team.

Hire Generalists, Not Specialists

Usually, it is difficult for SMBs to afford to hire a specialist for each position in their data science team, such as engineers, analysts, data scientists, visualisation specialists, etc., but it is also important to not risk damaging their output by understaffing key positions. Although it is ideal to have a team built with specialists, the solution for SMBs is to hire generalists and not specialists — more like a jack of all trades, master of none. 

A generalist will have an idea of all the positions mentioned and can provide insights to all the work processes related to it. It must be easier for larger companies to employ a director of analytics or a chief data officer to run the team, but for SMBs it is more important to cover all the positions to get a better view instead of hiring specialists at the start. Furthermore, without a specialist, SMBs can familiarise themselves with the necessary programs and software available in the market. Creating a generalised team also increases job satisfaction by creating more exposure for employees.

Understand Customers’ Problem

Businesses have always believed in giving customers the priority, and have been comprehensively meeting customers needs in order to generate positive financial results. But, your data science team never directly gets a chance to interact with consumers; they are only aware of the business unit looking for help with a specific problem. To improve efficiency and output, businesses need to focus on user stories. For that, the data science team needs to connect with customers in order to understand their story and use the same empathy to build the solutions. It is usually the stakeholders who are at the front lines, dealing with dilemmas directly impacting the bottom line, and by maintaining a sharp focus on partners’ goals, data science teams become intertwined with a company’s success and accountable for the impact. 

Providing a direct connection with customers will help the data science team to understand the problem better and create solutions specialised for them. Companies can also better interact and engage with their customers by analysing their feedback in order to improve a product or service. Data sources include traditional in-house data, social media, browser logs, text analytics, and large public data sets can be useful for companies to understand their customers.

Encourage Inter-Team Mentorship

Training can be an expensive affair for SMBS, also it requires an ample amount of time, but completely necessary in this dynamically expanding field. For SMBs to achieve a high-functioning, high-output data science team, they can easily rely on interterm mentorship, rather than relying on outside resources for training. SMBs should openly encourage knowledge-sharing among peers and teams and should develop infrastructure to support this goal. This can sometimes help team members to offload some of their technical skills, which, in turn, can create opportunities for them to expand into new skill sets without needing to hire fresh talent. Collaboration has always been a critical point in order to work efficiently. Also, working together, by sharing insights, will allow your team to tackle more significant problems, leverage individual strengths and avoid depending too much on one person.

Develop Scalable Processes

For SMBs, first data science projects can be fascinating, and to achieve initial results, it may force team members to work frantically in a disorganised manner. It is essential to winning the day, but a high-output data science team should always operate at scale or else the core goal of growth becomes a challenge. SMBs should create a scalable process in their business, which can withstand several factors and can combine efficiency with relevance where only the most critical steps are part of the process, in order to derive maximum output. Processes should be documented both in real-time and post the completion. In essence, this will be used to create repeatable, measurable processes that can help in answering relevant questions on features and model updation?

Identifying Trends

For SMBs to survive in this dynamic market, spotting and monitoring trends are imperative. Following behaviours and patterns of the technology and the market will allow companies to take a stab at predicting where the market is heading, or what is the demand of particular products or services that usually changes over time. Often, trend analysis and prediction have been instantiated out of the gut, but with the advent of big data, businesses can now use their data to do the guesswork of the process. Technology has been a dynamic and ever-evolving field and to survive with your small data science team, SMBs need to be aware of the data trends and market information to keep their relevance on.

The Never-Ending Feedback Loop

No technology experts, of today’s era, are working in silos; instead, they are integrated into the business sectors within an organisation. SMBs should encourage their small data science teams to incorporate feedback from experts, colleagues and customers in order to hone their craft continuously. Just like customer service, marketing, sales or any other functions within a business, the more responsive a data science team will be, the more resilient they become. Foster feedback should be a part of all SMBs culture. Companies that keep their organisations and data siloed, especially SMBs, may find it challenging to put insights into actions. SMBs that allow this never-ending feedback loop in their organisation will see more output and return of their investments from their small data science team.

The post How SMBs Can Maximise Output From Their Small Data Science Team appeared first on Analytics India Magazine.