The increasing amount of data available today is causing organizations to focus on developing a data-driven culture. As per research conducted by MIT, Microsoft, Google, and AWS, artificial intelligence automation and the way we analyze and predict data have further accelerated the need for organizations to be data-driven. Let’s take a look at some recent examples of how companies have been investing in data and digital transformation to stay competitive.
Data Growth and AI Automation:
With the rise of AI and machine learning, data has become a crucial asset for businesses. According to research by IDC, the total amount of digital data created worldwide is expected to grow to 180 zettabytes by 2025, up from 59 zettabytes in 2020. This growth in data is further accelerating the need for businesses to be data-driven.
Investment in Data-Driven Culture:
Companies that have invested in data and digital transformation have seen significant benefits. For example, Amazon has invested heavily in AI and machine learning to improve its recommendation engine, resulting in an increase in sales revenue. Similarly, Coca-Cola has invested in a data analytics platform that has helped them to analyze data from various sources to improve decision-making.
Data-Driven Products and Services:
In addition to using data to improve their internal processes, companies are also developing new data-driven products and services. For example, Rolls Royce has developed a platform that uses real-time data from its engines to predict maintenance needs, reducing maintenance costs and increasing engine reliability. Similarly, Walmart has developed an AI-powered shopping assistant that helps customers find products, resulting in increased sales revenue.
Data-Driven Decision Making:
To make the most of their data, organizations need to ensure that they have a data-driven decision-making process in place. This involves defining how data will inform business decisions and ensuring that everyone in the organization is aligned on corporate metrics. This is where the role of data visualization comes into play. Data visualization tools help organizations to easily digest and analyze complex data, making it easier to make informed decisions.
Key challenges for establishing a data culture:
- Fear of the unknown:
- First is the fear of scrutiny because everything is being measured more publicly.
- Second is the concern that becoming more numbers-focused will cause it to feel like a less personal work environment. Leadership must be able to address these concerns.
- Trust in the data/ data quality – in a data driven culture bad data can lead to unintended consequences where decisions are taken based on wrong information
- Lack of alignment top-down/ bottom-up and cross departmental on how to take advantage of the data
Definition of a data driven culture:
- Treating information as an asset:
- Data is the lifeblood of the organization and decisions are not based on intuition or at least accompanied with hard facts based on data insights
- Data is used in every aspect of decision making in the organization on C-Level, management and business analyst level and serving multitudes of data based objectives across departments
- Data is the new currency of the future where no data is disregarded and stored at least initially in the data lake landing zone to be processed further at a later stage
- In a data driven culture you have to be ready to confront the brutal facts: If the data tells you bad news, it should act as a stimulus to drive innovation, creativity and a proactive response
- Structured data is classified and enriched in corporate datamarts for easy self service data consumption
- Unstructured big data is enriched with metadata to enable data insights at a later stage through big data techniques including map reduce, predictive, noSQL, statistical and machine algorithm processing approaches
Data driven decisions – How to get from data signals to informed actions:
Successful application of data to decision making creates the need for flexible and scalable cloud and on premise data warehouse infrastructure for data ingestions, storage and processing through a flexible API, data and micro service fabric layer ease of data ingestion and data integration to other applications, connected edge devices and mobile apps.
Projection of data growth in the future:
According to a report by IDC, the amount of data in the world is expected to grow from 33 zettabytes in 2018 to 175 zettabytes by 2025. The report also suggests that by 2025, nearly 30% of the world’s data will be real-time data. This massive increase in data has made it challenging for organizations to analyze and use it effectively.
Research on the need for organizations to be data-driven:
MIT Sloan Management Review conducted a study that found that companies that adopt a data-driven approach are more likely to have a competitive advantage. The study also revealed that companies that use analytics extensively have a higher return on investment (ROI) than those that do not.
Microsoft conducted a survey of 400 executives and found that 87% of them believed that data-driven insights were critical to their organization’s success. The survey also found that companies that used data analytics extensively reported higher productivity and profitability.
Google’s research on the use of data-driven strategies found that companies that relied on data and analytics were more likely to make better decisions, reduce costs, and improve customer satisfaction. The study also revealed that these companies were more likely to outperform their competitors.
AWS conducted a survey of 1,300 IT and business professionals and found that 88% of them believed that data-driven decision-making was important to their organization’s success. The survey also found that companies that used advanced analytics reported higher revenue growth and profitability.
- Ford Motor Company: During the 2008 recession, Ford invested heavily in data-driven strategies and emerged as one of the strongest car companies after the recession. The company used data analytics to optimize its supply chain, reduce costs, and improve customer satisfaction.
- Amazon: Amazon is a prime example of a data-driven company. The company uses data analytics extensively to optimize its operations, improve customer experience, and drive innovation. Amazon’s success can be attributed in part to its data-driven culture.
- Netflix: Netflix is another company that has invested heavily in data-driven strategies. The company uses data analytics to understand its customers’ preferences, personalize its content offerings, and improve its recommendation engine. Netflix’s success can be attributed in part to its ability to use data to stay ahead of its competitors.
Operational considerations for data driven organizations:
With all that being said – as with all things “everything is good in moderation” is a principle that also applies here.
- How is the data collected and shared across the organization?
- What data is being collected vs what data is discarded? e.g. what events of event stream data should be stored at the device vs. data lake level
- Define how the data will inform business decisions – the alignment on corporate metrics across the organization is key here
- At what frequency is data needed to make actionable decision?
- How is the data insight visualized so it can be easily digested, analyzed, drilled into and reacted to.
Limitations of analysis based on data only:
- “Analysis paralysis” when approach of focusing on nothing but current business performance metrics can result in a loss to innovate effectively
- Some things cannot, nor should be measured without seeing the big picture and applying common sense
“Not everything that can be counted counts, and not everything that counts can be counted.” Albert Einstein
In today’s data-driven world, organizations need to invest in data and digital transformation to stay competitive. By doing so, they can improve their internal processes, develop new data-driven products and services, and make more informed decisions. However, it’s important to remember that data analysis has its limitations and should be used in combination with human judgment. As the amount of data available continues to grow, businesses that are able to effectively use data to drive decision making will have a significant advantage over those that don’t.