Data driven decision is normally described to involve interpretation and use of data analysis to lead in the decision of the business. In this article we look in details at key elements that influence decision making that is data driven.
Data Collection
This is the process of collecting information from different sources for them to be analyzed and used in making an informed decision. Some important key aspects in data collection process include:
Types Of Data
Quantitative Data.
This is data which is numeric and can be quantified as well as measured.
Qualitative Data.
This refers to data that is descriptive and gives insights on behaviors, motivations and opinions. For instance, open-ended survey responses and interview transcripts.
Sources Of Data
1. Primary Data.
Data collected straight from the source with the aim of particular research purpose. Some primary data includes;
Interviews: This involves one on one talking or having conversation to have information which is detailed.
Observations: Its recording and watching events or behaviors as they happen.
Experiments: Tests that are controlled and are meant to learn about the effects of the variables.
2. Secondary Data.
This is data that is collected already and is ready for use.
Academic Journals; These are the research papers and studies that are published by scholars.
Online Databases; Data repositories such as PubMed and Statista.
In order to make data driven decisions it requires one to follow a systemic approach in making those decisions.
Definition Of The Objective
This is about outlining clearly the problem you want to solve and the necessary choices that has to be made. Therefore, the objective has to be measurable, specific, relevant, and achievable and time bound.
Collection of important data.
Gathering of relevant data from different sources is important. This could mean having external data (market trends) and internal data (customer feedbacks, sales records).
Prepare and clean Data.
The data collected should be complete, accurate and correctly formatted. This revolves correcting errors, duplicate removal and handling values that are missing.
Data Analyses
It’s the use of technical and statistical tools to have the data analyzed the right way. This could involve hypothesis testing, regression analysis and descriptive statistics.
Data Visualization.
This is the creation of visualizations to assist in data interpretation and patterns identification as well as outliers and trends.
Interpretation of the Results.
Interpretation Of The Results
It’s important to draw insights from data which has been analyzed. It’s good to be aware on what the data is saying about the decisions and problem at hand.
Make Decision.
Decisions are made from the insights attained from the analysis of the data collected. Its therefore important to consider the possibility of potential benefits and risk brought by the decision.
Decision implementation.
This is all about putting the decision into action. Therefore, the plan is clear when it’s about responsibilities, implementation and timelines.
Evaluation And Monitoring
When the decision implementation is completed, the next phases is usually results monitoring to check whether the outcomes achieved are desirable. This is when decision effectiveness is evaluated and the necessary adjustments are made.
Sharing of the document.
This is now the final stage of the process of decision making, used data, performed analysis and the results.
Additional topic on making data driven decisions includes:
Data Analysis Techniques
Descriptive Analytics.
This is a program followed to analyze data which is historical for the understanding of what happened in the past. It’s about interpreting and summarizing processed information and insights that can assist understanding of the business trends and performance over a period of time. Here are important elements about analytics which is descriptive;
Data collection.
This is about collecting data from different sources such as spreadsheets, databases as well as providers of external data.
Cleaning of data.
For data to be consider to be appropriate, consistent, and accurate and error free it has to through the cleansing stage.
Aggregation of data.
It’s about bringing and organizing data from different sources to appear in a format which is usable.
Data Visualization
Graph, charts and any other visual tools are put to use to effectively present data in a manner which is easily understandable.
Descriptive analysis is considered to be important since it assist organizations in ;
Catching trends as well as patterns.
Use of historical data to come up with informed decisions.
Clearly understanding of performance of the past.
Upgrade on the business operations and processes.
The common tools used in these descriptive analytics include Power BI,Excel ,Tableau as well as other python libraries.
Diagnostic Analytics
This kind of analytics is more advance and involves examining content and data with the aim to answer why did it happen. Techniques that are involved include data discovery, drill-down, correlations to unravel the cause root of behaviors or events. Below we look into some aspects that are key in diagnostic analytics.
The used Techniques In Diagnostic Analytics Includes;
Correlation Analytics. This is about identifying different relationships that exists to check whether there is correlation between one or more variables.
Root Cause Analysis. It’s about observing and conducting investigation that causes issues observed or anomalies.
Drill-Down Analysis; it’s about reducing or breaking data into details that are finer for understanding particular aspects more deeply.
Regression Analysis: Refers to modeling relationships that exist between independent and dependent variables for understanding impact resulted by different factors.
Benefits
Based on insights that are data driven it enhances the process of decision making.
Improved capability to process and identify promptly issues that could be available.
Better knowledge on trends and patterns that exist.
Improved effectiveness and efficiency in strategies and operations.
Predictive Analytics
It’s about use of statistical algorithms, historical data, learning of machines techniques in order to identify the future likelihood based on the results of the previous data. At times it’s used to predict about what could happen in the coming days through trends and patterns analyzing from both the present and the past.
Predictive Analytics Applications
Healthcare; Disease outbreaks prediction, outcomes of the patients and readmissions of the hospital.
Finance: Credit scoring. fraud detection and assessment of investment risk.
Retail: Demand forecasting, management of the inventory and marketing that is personalized.