Data Analytics for Decision Making

The Harvard Business Review reports that according to a research carried out by BI company Microstrategy, titled “The Global State of Enterprise Analytics (pdf)”, 56 percent of respondents said data analytics led to “faster, more effective decision-making” at their companies.

How then can you use data to your advantage and reap these benefits at your company? Learning how to successfully analyze data may help you come up with inferences, projections, and actionable insights to help you make better decisions.

The last decade has witnessed a surge in business enthusiasm for data-driven decision-making. With digital disruption surprising and sometimes upsetting even the most well-known brands, corporate organizations have been given a stark reminder that the only constant is change — and insights are needed to go forward.

For organizations that have embraced the use of data and analytics, a common set of benefits has emerged that continue to motivate their investments, including improved efficiency and productivity, faster and more effective decision making, and better financial performance.

WHAT IS DATA ANALYTICS?

Data analytics is the science of analyzing raw data to make conclusions about that information. It is the practice of examining data to answer questions, identify trends, and extract insights. Data analytics techniques can reveal trends and metrics that would otherwise be lost in the mass of information. This information can then be used to optimize processes to increase the overall efficiency of a business or system.

Manufacturing organizations, for example, frequently record the runtime, downtime, and work queue for various machines, then analyze the data to better schedule workloads so that the machines function at close to peak capacity.

To get the greatest insight from your data, \you need to familiarize yourself with the four key types of data analytics.

Types of Data Analytics

1. Descriptive analytics: This describes what has happened over a given period of time. This is the simplest type of analytics and the foundation the other types are built on. It allows you to pull trends from raw data and succinctly describe what happened or is currently happening. The biggest use of descriptive analysis in business is to track Key Performance Indicators (KPIs). KPIs describe how a business is performing based on chosen benchmarks. Business applications of descriptive analysis include KPI dashboard, Monthly revenue reports, Sales lead overview, etc.
2. Diagnostic analytics: This focuses more on why something happened. This involves more diverse data inputs and a bit of hypothesizing. Did the weather affect beer sales? Did that latest marketing campaign impact sales? The diagnostic analysis takes the insights found from descriptive analytics and drills down to find the causes of those outcomes. Organizations make use of this type of analytics as it creates more connections between data and identifies patterns of behavior.
3. Predictive analytics: This moves to what is likely going to happen in the near term. What happened to sales the last time we had a hot summer? How many weather models predict a hot summer this year? Predictive analysis uses the data we have summarized to make logical predictions of the outcomes of events. This analysis relies on statistical modeling, which requires added technology and manpower to forecast. It is also important to understand that forecasting is only an estimate; the accuracy of predictions relies on the quality and detailed data.
4. Prescriptive analysis: This is the frontier of data analysis, combining the insight from all previous analyses to determine the course of action to take in a current problem or decision. Prescriptive analysis utilizes state-of-the-art technology and data practices. It is a huge organizational commitment and companies must be sure that they are ready and willing to put forth the effort and resources.

DATA-DRIVEN-DECISION-MAKING (DDDM)

Data-driven decision-making (DDDM) is defined as using facts, metrics, and data to guide strategic business decisions that align with your goals, objectives, and initiatives. When organizations realize the full value of their data, that means everyone is empowered to make better decisions with data, every day. However, this is not achieved by simply choosing the appropriate analytics technology to identify the next strategic opportunity.

Your organization needs to make data-driven decision-making the norm — creating a culture that encourages critical thinking and curiosity. People at every level have conversations that start with data and they develop their data skills through practice and application.

It also requires proficiency, creating training and development opportunities for employees to learn data skills. Finally, having executive advocacy and a community that supports and makes data-driven decisions will encourage others to do the same.

Establishing these core capabilities will help encourage data-driven decision-making across all job levels so business groups will regularly question and investigate information to discover powerful insights that drive action.

Steps to effectively make data-driven decisions

1. Identify Business Opportunities: This step will require an understanding of your organization’s executive and downstream goals. This will help you later in the process to choose key performance indicators (KPIs) and metrics that influence decisions made from data — and these will help you determine which data to analyze and what questions to ask so your analysis supports key business objectives.
2. Survey business teams for key sources of data: Valuable inputs from across the organization will help to guide your analytics deployment and future state — including the roles, responsibilities, architecture, and processes, as well as the success measures, to understand progress.
3. Collect and prepare the data you need: Accessing quality, trusted data can be a big hurdle if your business information sits in many disconnected sources. Once you have an idea of the breadth of data sources across your organization, you can start data preparation. Start by preparing data sources with high impact and low complexity. Prioritize data sources with the biggest audiences so you can make an immediate impact. Use these sources to start building a high-impact dashboard.
4. View and explore data: Visualizing your data is crucial to DDDM. Representing your insights in a visually impactful way means you’ll have a better chance of influencing the decisions of senior leadership and other staff. With many visual elements like charts, graphs, and maps, data visualization is an accessible way to see and understand trends, outliers, and patterns in data. There are many popular visualization types to effectively display information: a bar chart for comparison, a map for spatial data, a line chart for temporal data, a scatter plot to compare two measures, and more.
5. Develop insights: Critical thinking with data means finding insights and communicating them in a useful, engaging way. Visual analytics is an intuitive approach to asking and answering questions of your data. Discover opportunities or risks that impact success or problem-solving.
6. Act on and share your insights: Once you discover an insight, you need to take action or share it with others for collaboration. One way to do this is by sharing dashboards. Highlighting key insights by using informative text and interactive visualizations can impact your audience’s decisions and help them take more-informed actions in their daily work.

Data-driven decision-making is a game-changer. When everyone in a business embraces visual analytics, data becomes a valuable organizational asset. Data-driven decision-making becomes a company mission, not a burden, with a contemporary business intelligence solution. As a result, quicker and more informed decisions are made. These decisions will result in a stronger bottom line, greater creative and commercial success, and more staff involvement and cooperation.