Although data is considered one of the key assets today, most companies are not able to leverage it or create a Smart Analytics strategy. A report by Accenture revealed that 68% of the organizations surveyed cannot generate tangible and measurable value from their data.
In a recent research, Forrester highlighted a similar insight: between 60% and 73% of business data is not used for analysis or strategic purposes.
This problem can become critical for companies because it slows down their innovation at the business, product, and customer relationship levels. In the same way, having such a valuable asset as data available and not taking advantage of it entails great costs for organizations.
Let’s take a look at why data is not being leveraged
Experts agree on 3 main reasons why this problem is occurring in the world:
- Over time, companies have been accumulating lots of data (structured or not), sometimes fed in real-time. This information is processed from a strategy that involves Multicloud but does not use Smart Analytics, which means that it is not creating value.
- Many companies consider that processing data in a Data Warehouse or using a SQL tool is enough, but it is not. To take real advantage of the information it is important to use technologies such as Machine Learning and AI, as they provide enormous capabilities to analyze, innovate and make data-driven decisions.
- Data is not reaching all members of the organization, possibly not reaching many decision-makers either, or arriving late. This creates a problem when making critical or strategic decisions.
Innovation with Smart Analytics
Smart Analytics is a capability that allows you to analyze a large amount of data (such as we have today) intelligently and focused on decision-making and innovation.
This capability uses technologies such as Machine Learning, AI, and the Internet of Things (IoT) and Cloud Data tools to drive business.
Doblin, a subsidiary of Deloitte, describes 3 types of innovation that can be created with Smart Analytics:
Configuration: That focused on the internal operations of the business.
Offering: This is related to the product or service.
Experience: It is about how consumers interact with products, services, and the business.
The Unilever story
The first step to innovate is to understand what customers and users value about your product or service. This is when data guides the business or sales model to meet customer desires.
An interesting example is Unilever. Harvard Business Review described how this company used AI to automatically and quickly analyze their customers’ opinions and comments (captured as text data) to understand how they were engaging with the products and the brand, as well as what their needs were.
The result? $550 million in total benefits (incremental sales and cost savings). The use of AI and Smart Analytics also allowed them to launch more than 2,000 projects in one year, which were intended to find value-generating opportunities for the company.
Unilever did not stop there, as they are also implementing Smart Analytics in their marketing area. We will have to see how their final experience will be!
Value is not generated immediately!
Driving business innovation through a Smart Analytics strategy is not a process that happens automatically. Typically, a Data Analytics project requires a strategy, a goal, collecting data and validating it, converting data into strategic information, and finally incorporating it into the workflow.
At Apiux, we have experience in foundational and Cloud migration projects, as well as in Smart Analytics and data collection. We invite you to contact us to support your organization in generating tangible and measurable value from data.