As we already know, organizations have ability to collect, store and analyze massive amount of data from multiple sources. They have spent a lot of money managing this big data. Investments in technology and analytics are useless unless employees can use data, information and knowledge in their decisions-making. Many companies are facing a big challenge in data-driven decision-making. Good and rich data won’t quarantee good decisions on their own. I posted May 2012 an article about Insight IQ. It was based on April 2012 issue of the Harvard Business Review where Shvetank Shah, Andrew Horn and Jaime Capella argue that Good Data Won’t Guarantee Good Decisions. In this article I continue this theme a bit further.
Shah, Horn and Capella found that there are three main types of decision makers:
- Visceral decision makers (gut feel decisions)
- Unquestioning empiricists (rely on the number alone)
- Informed skeptics (gut feel with data)
If we consider the matter in data perspective I defined simple data collaboration or data insight maturity scale. It emphasizes level and maturity of data collaboration in decision-making.
Organizations on the right ‘high side’ are capitalizing the data in decision-making. They combine the gut feel with data and can drive decisions that make a real difference in productivity and profitability. They can drive real competitive edge from and behind the data. They drive all necessary information from markets for better decision-making. They know their customers and customer journey. Although the decisions can be very complex with limited amount of time and there are many options to choose from, they can still make the decisions and carry out them fast. These companies are the winners. They have hit the jackpot, but unfortunately I see these kinds of companies quite seldom. However, that is the goal to go for.
Another way of looking at this variety in corporate behavior is to divide companies in three categories according to state of data maturity. There are three types of companies: data collectors, data analyzer and data innovators.
1. Data collectors
These companies are collecting and learning which data is important for their business. Light segmentation is used and it is more building and focusing on different customer groups. Analytical skills are very low and decisions are made as gut feel or based on basic sales figures and income statement
2. Data analyzer
These companies have systematic way of collecting and storing data. They are invested in the technologies that are available to analyze the data. They can do basic business reporting but are not able to drive the real value behind the data. They are good at analyzing and reporting but suffer the lack of interpretation skills to drive deeper business insight.
3. Data innovators
These companies are more comfortable with big data sets and technology. The focus is in how benefit from actionable insights and strategic big data. They have effective ways to share business information across functional areas that better decisions can be made. They have many data-savvy employees and data-driven decisions are informality in C-Suite.
Steps from data collector to data innovator
It is all about people, process and technology. Here are six steps to improve data-driven decision-making.
1. Emphasis on right data
- collect all relevant information into one single platform
- from business understanding to data understanding
- automate the data collection process
2. From silos to holistic business view
- map all the relevant data, which is essential for decision-making
- data preparation, consolidation and visualization are necessary
- CMO & CIO partnership
3. Choose the right technology
- find technology that meets your business demands
- choose technology, which accept and read data from any source
4. Online reality
- today’s business and business environment needs real-time data
- online data as enabler of quick decisions and insights
- rapid resource allocation
- online business management and intelligent decisions
5. Create strategic marketing dashboard
- KPI’s by channel
- true values from returns and investments
- identify risks and opportunities
- measure and lead the customer journey
6. Create systematic analytic skills competence development program for employees
- find the data-savvy workers in your organization
- make them to train everyone else of their analytics skills and method
- leverage analytics know-how widely in your organization
- C-suites as exemplary for others