Data-led insights can help business managers in proactive decision-making. They not only improve the day-to-day operational decision making, but also strategic decision making. Data can empower business managers significantly during business and digital transformation initiatives by helping them improve their understanding of customers, products and marketing spend.
Alongside digital transformation, we now see the development of ‘data transformation’, a more efficient way of collecting, cleaning and analyzing data. The two processes are tightly connected, as no digital transformation can succeed if data is not ready for business problem solving and insight generation. Having said that, one must also realize that this is a journey. Data can never be perfect in a growing business. Business managers need to take decisions and data must enable that. It’s therefore important to run both processes - 1) continuous improvement in data foundation and 2) building more analytics maturity to enable effective decision making – in parallel to each other.
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Training and engagement: empowering the workforce
It is becoming apparent that data, from collection to analysis, has become an important component for businesses around the world, regardless of the sector. This is having a growing impact not only on organizations but also on employees. Those who, within an organization, are directly involved with applying data to the company processes can be considered data analysts in some way.
We can always divide the organization into a pyramid. At the bottom are 'novices'; they are using spreadsheets like Excel and lot of reports or dashboards for day-to-day decision making. 60% of the organization can be novices. Then there are data 'semi-professionals'; they know a little bit about data-handling tools like SQL or visualization tools like Tableau, which enables them to go beyond Excel and use data more effectively. They make for 15% of the organization. Then you have data 'professionals', who have the knowledge of precise data science techniques and tools to enable accurate and proactive decision making. There are 10% of such professionals. Finally, you have the data 'champions' at the top; they evangelize data-led thinking in the organization and lead transformation projects. They comprise of up to 5% of the organization.
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Mindset over tools
While a differentiation of duties and levels of knowledge within the workforce can help the organization to be more efficient, it could prove highly beneficial to make sure that everyone is trained to productively use data.
I feel this is more of a social challenge than a technical challenge. We often think that without advanced tools and techniques training, we cannot use data. However, a lot of the time it has been seen that in spite of heavy investments in this area, results are minimal. This will continue to happen until individuals develop the belief in the power of data. Those who believe should try simple things to start in order to make sense out of the organization data lying all around them.
Remember, it is not the insight generation process that is most important, it is rather what actions you take based on these insights. Rather than focusing on trying to perfect a tool or technique, one must just start to use data in small ways for daily decisions. Organizations need to hold workshops that show individuals how data can be leveraged in their own business functions. Those individuals must also be keen and show curiosity rather than trying to find reason why data-led thinking will not work for them as the data, tool or technique is not perfect. One must be keen to get started. Its a long journey, hence all the more reason to start now.
Value to internal resources
Additional support to an organization looking to improve its data-driven decision process can come from the IT team, who can provide tools and further technical insights. There can be an analytics adoption strategy. First, organizations must realize it cannot be one size fit all. There are different needs different levels of employees and managers. Some might need tool and techniques training like engineers.
Business managers, on the other hand, need workshops that show them how data can solve their current challenges. Business heads need to define projects where small teams of managers and engineers are brought together to solve a particular business problem. This can be a great way to complement the training and workshops.
Until value is taken from data generation and training becomes the core focus, analytics adoption can never take place.
Things you must remember
Key things that one must keep in mind when implementing any analytics project
1. It is an opportunity for the business managers to add – perspectives of data management, analytics solution framework, advanced algorithms, principles of data visualization and project implementation to existing ways of solving business problems using intuition, creativity, common sense and domain knowledge.
2. It is important to bring the algorithm close to the point of decision-making. While a lot of time is invested in building a great analytics solution, often adequate focus is not applied to close the last mile gap between solution implementation and it's usage.
3. Use data to listen better. Understand the available data without focusing on what is not available. Demand information, perform analytics and draw insights to realize the behavior change in your customers. Improve decision-making by finding out 'Actionable Insights' on how to serve your customers.
4. Change management is key. Embed analytics in the fabric of the company. Focus on demystifying it and linking it to practical stuff. Start with a business problem at hand and work backwards rather than looking at the data and thinking what to do with it. One must be prepared to face resistance.
5. It doesn’t happen overnight. It takes a while to build the data and analytics foundation. It takes even longer to build capabilities within teams, a culture of data-led thinking and finally success stories. So it's important to start now.
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Jaydeep Chakraborty, Instructor in Data Analytics, Toronto School of Management