With the increasing amount of data in modern businesses, data science has been receiving a lot of attention. A growing number of companies are, nowadays investing in data science researchers and experts to implement technologies like artificial intelligence and machine learning in their organisation in order to derive actionable insights. But, to place such a massive transformation in an organisation, one has to ensure complete business readiness for data science. Although it is interesting to imagine the potential benefits data science can provide for your organisation, it is worth evaluating how much your organisation is prepared to accommodate a team of data scientists.
In this article, we would talk about what a data-driven organisation looks like, and how to work towards achieving that.
What A Successful Data-Driven Organisation Looks Like
Although its 2020, and data is the new oil for the era, several companies are still striving to become data-driven. But, what does ‘data-driven’ actually mean? An organisation needs to install the right tools and applications to become data-driven; however, it is even more important to make data and analytics a part of organisations’ day-to-day business strategies. According to a report, companies that are harnessing data to gain business insights are 23 times more likely to acquire customers and have even more chances of retaining them and getting profitable results.
To have a data-driven organisation, one has to create a mindset and a culture within their organisation, where data analytics creates the base of making all business decisions and embraced by all levels of the organisation. A data-driven organisation would aim at enabling every employee of their organisation to gather and use data for doing business.
Although there aren’t any stipulated paths to becoming a data-driven company, there are a few best practices and common features that could help organisations to apply more data in their daily business process.
The Revolution of Data Science Begins At Home
While several factors can contribute to creating an organisation that is data science ready, one of the most critical factors is the change in mindset. It has been proved to be the hardest challenge to shift a collective mindset of the organisation to embrace data.
The transformation of an organisation to be data science ready starts from the top including CEO and other c-level executives appreciating data as the basic elements of the business to every employee getting involved in using data in order to manage their departments. The leaders and the executives of the business are responsible for making this cultural change and supporting the initiatives, which in turn will push employees at every level to think differently and weave data analytics into their workflow. Usually, organisations fail at this stage because the new approaches usually don’t align with the current business process, which in turn will create disorder in the business goals.
Apart from senior management leadership, this transformation also requires large scale funding, seamless technology infrastructure, and significant operational change. To lead by example, senior management leaders should carve out strategies, budgets and also hire new analytical tools for the organisation. The leadership team needs to be prepared for this data transformation and exemplify actions and behaviours that they want their employees to emulate.
Sharpen Your Resources
Over time, organisations can create a data-oriented culture by educating their leaders and employees; however, developing relevant analytical tools that can be utilised appropriately by every level of the organisation is also important. Using the right tool and platforms to get actionable insights is exceptionally crucial for organisations to gain a competitive advantage. From departments of marketing and finance to operations and HR, business teams require self-service analytical tools to simplify data preparation and speed up analytical tasks. These tools need to have built-in advanced techniques like machine learning which can be used from analyse data and monitor it regularly. If an organisation can democratise their data, it will provide a lot of opportunities for employees to grasp, and will allow them to leverage data regularly. This, in turn, will free up your data scientists to focus on more complex strategic projects.
Another critical factor is to create domain readiness. For domain readiness, organisations should not only hire new talent but also train their existing talent pool in using modern analytical tools and should ensure proper utilisation. This would involve an understanding of the complex data landscape of the organisation, which in turn would help employees to acquire the right data to obtain desired insights.
Frame The Right Business Question
To get the maximum benefits from your data science team, organisations should ask the right question, which can have the correct relevancy to your business. In this cutthroat digital age, setting the right data questions is extremely important for the overall success of the business. The questions should portray an organisation’s best-informed priorities, which can be solved by the data science teams. Some examples of good questions are, “how to reduce business costs?” or “how to increase your company’s revenue?” With these posed questions, organisations should align their key business functions and domains with their most important use cases.
In the real world where funds and time are significant constraints for businesses, analytics could be of great help, only if asked the right framed question. Experts believe that asking the right question is an art and should always be posed in such a way that requires more than a “yes or no” response, which helps in keeping the dialogue open for more information. Apart from gathering all existing data and information, it is also important to prepare the data for proper utilisation in order to develop a successful business strategy.
Build An Effective Data Science Team
To initiate data science and predictive analytics in your organisation will require a comprehensive understanding of the space and the structure of the team. Usually, the structure of the team varies based on the size of the company and the requirements involved. Although organisations, nowadays, extensively seeking a competitive edge by hiring data scientists to draw actionable insights from data, building a data scientist team will require a strategic approach and framework of the output needed from the team.
The most critical factor for an organisation is to build an autonomous data science team with balanced skills which can cover and work on data science projects from end-to-end. A capable data science team should include data scientists who can focus on analysing data and testing statistical models; software engineers who can focus on making efficient and maintainable codes; data engineers who can focus on managing datasets, databases and create scalable infrastructures; and product managers who have expertise in specifying requirements and aligning them with business functions and other teams. Another critical aspect of creating an autonomous team is to give them enough liberty and freedom to play around with the data and choose the tools to work with.
Measure Data That Is Relevant
Data is the foundation of data science. However, due to the increasing volume and volatility of data, it gets imperative for organisations to find the correct relevancy and quality of data to derive actionable insights. Organisations, usually, fail to discover their core priorities and therefore, the data used to derive insights don’t align with their business outcomes. One of the main ways to gather relevant data is to ask relevant questions. Every company produces a massive amount of data; however, having access to technologies that can process and analyse the abundance of data is a short duration is difficult. Therefore collecting and gathering relevant data is crucial for organisations to fast track their process.
Relevant data not only helps in making better decisions but also assists in solving problems, monitor business performance, and also improve business processes for the future. Relevant data also allows businesses to identify and understand consumers and the market. It is believed that the clearer businesses see their customers, the easier it gets to reach them.