Advanced Data Analytics

How to transform into a data-driven organization?

Our latest advanced data analytics blog post covered the benefits of applying the data-driven approach and presented a few takeaways on how to go from culture to data to action. This one is written to dig deeper into the process of becoming a data-driven organization.

The opportunities created by digital transformation affect how businesses run and impact the lives of consumers. However, as organizations adapt to become more data-driven, they must address many challenges on their journey.

Implementing business intelligence (BI) and data & analytics (D&A) means shifting from tactical data delivery to strategically filtering and extracting value from financial and operational data, then converting it to meaningful information that supports business decisions.KPMG

What is a data-driven company?

It's the one that empowers its employees with the skills and tools to analyze data to drive decisions and strategy. The one that uses data to become descriptive, predictive, and prescriptive so as to leverage the benefits of each type of analytics. 

Keep in mind – analytics should not be the goal, but rather it should be a strategic enabler. The following steps will help turn it from an idea into reality

  1. Get the basics right
  2. Adopt a data-driven mindset
  3. Develop an integrated data warehouse
  4. Manage your data
  5. Turn insights into action
  6. Gain a competitive advantage

Let's look at each step more closely. 

1 Get the basics right

Becoming a data-driven organization is a process that must be continually modified based on changes in your markets, your business, and technology. The primary step is to create an all-important strategy.

What makes a good data-driven strategy? Start with defining a SMART goal, and add the milestones that will help you keep track. Ask yourself: What can data do for me? Look at the specified business objectives, and build a strategy around them — that way, you won’t be dazzled by all the possibilities big data has to offer.

2 Adopt a data-driven mindset 

In the 2021 survey, 92.2% of mainstream companies report that they continue to struggle with cultural challenges relating to organizational alignment, business processes, change management, communication, people, skill sets, and resistance or lack of understanding to enable change. Harvard Business Review

Building employees’ skills and ensuring they know how to utilize the analytics tools is crucial, but with technical changes come the cultural ones, too. Adapting to a data-driven mindset is the second most necessary thing. It should not be difficult, but it requires some time.

For a start, get everyone up to speed with your data project. Creating a feedback loop will help you read the data more accurately and pull everyone’s performance up. Remember to keep asking your team for feedback during each step of the process.

To make a mindset transition as smooth as possible, Harvard Business Review suggests doing the following three things:

3 Develop an integrated data warehouse

Businesses generate a vast amount of data every day. The primary purpose of data warehouses is to bring together data from different formats, sources, and applications to derive insights richer than from a single source. Locate and identify all the data sources useful for your defined strategic goal. It even helps to get third-party data from retailers and business competitors. This holistic view of the business is the third most important thing.

 

 

4 Manage your data

Once the holistic view of the business is achieved, it’s essential to invest in a practical framework for reliable data management which can address a range of tasks. Pick a user-friendly integrated tool that can provide help to your team on a daily basis.

For example, creating interactive dashboards with Power BI makes data easily accessible in one picture across the organization. It helps consolidate and visualize the data to make well-informed decisions by giving a real-time snapshot in any desired moment. Besides, creating relevant and timely executive dashboards helps to measure success and drive strategy.

After a successful data management, you will have answers to the three of five initial questions – What happened?, Why did it happen? and What might happen?.  A triumph. That's what we call this moment.

5 Turn insights into action

Don’t stop your data-driven transformation after turning data into insights. Combining insights with machine learning will yield even more value from the data. For instance, you could make predictions based on complex data patterns, make sense of a broad range of structured and unstructured data and apply that knowledge to business planning, budgeting, forecasting, and decision support.

Applying AI this way will speed up your decision-making process and make it more accurate. Some of the most suitable use cases for making decisions without human involvement are the ones made hundreds of times over with the same type of data, such as pricing, loan decisions, and risk reviews.  

Consequently, you will be able to address two more questions – What should I do? and How should I do it? 

6 Gain a competitive advantage

It is to become leaders in the industry that companies invest more and more resources to become data-driven. It helps the management to implement policies and strategies which are helpful for the development of the company. Benefits include bolstered organizational activity, enhanced efficiency and effectiveness, accurate decision-making, and revenue growth.   

Analytics is the foundation for a differentiated customer experience and more meaningful engagement. – Deloitte

 

The most important takeaway

Transforming into a data-driven organization takes time. While establishing groundwork to reach the strategic goal, it is necessary to identify smaller targets. Demonstrated quick wins that validate your analytics strategies will provide you a better understanding of the whole process. The actions generated from these insights enable you to re-imagine the way to the business goal you have set at the start of your data-driven transformation.

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