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Data-Driven Intelligence (DDI) refers to the practice of making strategic decisions by collecting existing data methodically, analyzing it, and extracting meaningful insights to have an accurate representation of reality.
The approach allows you to unlock your data’s potential, enabling decision-making based on facts rather than gut, instinct, emotion, or past experiences.
The benefits of data-driven intelligence
Data-driven intelligence is an umbrella term that encompasses big data, Business Intelligence (BI), data analytics, and data modeling.
At Datavid, we like to shorten it simply to “data intelligence”.
So what are the benefits of DDI?
- It helps management be more confident in making decisions as it is an objective and qualitative process, eliminating any biases or assumptions.
- DDI increases transparency and accountability as qualitative data lends support to management decisions. This trickles down to all employees.
- It can be used across organizations of any size—from startups to enterprises—facilitating change management and fostering innovation.
- The data makes it easier to monitor incremental changes, facilitating early detection of business opportunities and response to market changes.
Just like any new process though, DDI requires a bit of homework and implementation. If you want to invest in it, it’s important to get it right from day 1.
5 steps to implementing DDI effectively
Data forms a crucial part of any organization yet implementing a “data-driven” approach remains elusive. A reason for this is that a lot of people tend to have a “data denial” mentality, where their assumptions become stronger than facts.
Most organizations struggle when dealing with large volumes of data, especially if their goal is to make it a universal basis for decision-making.
This is why we’ve planned out 4 key steps for you to follow:
Step #1: Setting goals and objectives
Goal-setting starts at the very top and flows downstream. This sets an expectation that decisions should be driven by data. Goals should be specific and measurable.
Having a closer look at business objectives to outline what we have set out to achieve is the next step. Then comes building the strategy around the objective.
Step #2: Collecting data
Collecting data is probably the most important part of the entire process. It may come from external or internal sources, integrated or standalone.
It could also be qualitative as well as quantitative.
At this stage, you need to determine the data that is available and unavailable based on what your primary goals and objectives state.
Step #3: Analysing the data
This will require upskilling the employees, especially non-technical people. They might be unwilling to venture into something completely new.
Specialized training is required to analyze data both from a quantitative and qualitative perspective. The latter is best tackled by a subject matter expert.
Step #4: Extracting and presenting insights
This is the final and most important step. How the findings are presented and to whom is the key to the entire puzzle.
Do the insights align with the goals you set out to achieve? Having them presented visually and outlining the risks (and rewards) will help decision-makers immensely in rooting a decision.
Leveraging DDI for your enterprise company
Becoming a data-driven organization is transformational, and it pays dividends. Trickling down from top management, DDI makes data the central asset to leverage for growth.
This leads to faster and more informed decisions.
Companies like Datavid enable data intelligence across departments.
Thanks to solutions like Datavid Rover, you can simplify data ingestion, processing, and analysis by automating manual tasks—lowering expenses and adding value.
Frequently asked questions
Data-driven intelligence technology applies advanced analytics and machine learning to extract insights from large data sets, enabling organizations to optimize operations, make informed decisions, and gain a competitive edge.
The different types of data intelligence include descriptive intelligence, diagnostic intelligence, predictive intelligence, and prescriptive intelligence.
An example of data intelligence is using customer purchase history and preferences to create personalized product recommendations.