A CIO’s Playbook for Harnessing Big Data

by IT Executives Council | Aug 16, 2024 | Big Data

Today’s CIOs understand data’s integral role in decision-making. However, harnessing that data can be complicated. How can leaders control and make use of the large amounts of information provided?

CIOs require a combination of tools and strategies to review data and extract various insights. This article will serve as a playbook for harnessing big data and using it to make smart decisions.

Centralizing Data

One of the biggest challenges in harnessing data is centralizing it. It is typically spread over various systems such as CRM, EPR, and POS marketing automation platforms. It may also be in various locations such as cloud, edge, and public storage.

Data centralization does more than make your data more accessible. It reduces duplication, increases the efficiency of processing and analysis, and improves quality and accuracy. It makes data more secure, agile, and adaptable and improves customer and stakeholder experiences.

Companies must find the best system for data centralization based on their needs and data assets. They must create a plan with defined processes, standards, and a governance framework. Then they should develop a plan for migrating to cloud storage platforms.

There are several tools available for centralizing company data as follows:

Big Data Warehouse: A big data warehouse collects and manages various types of data. It ensures consistent data quality, format, and accuracy. The warehouse is organized into tiers that utilize different techniques to process data for practical use.
Cloud Data Lakes: Data lakes collect, store, manage, and analyze processed and unprocessed data making it easier to derive insights. They also provide additional features like ingestion, cataloging, indexing, and security so you can manage data efficiently.
Database Systems: Database systems are ideal for data centralization. They fall into three main categories; rational models which use tables to store data, NoSQL databases which use flexible data storage models, and object-oriented databases which store data as objects with properties and methods. Determine which is best suited for your company.
Analytics Tools: Ad hoc analytic tools allow companies to slice, share, and visualize data. They create customized reports that are ideal for presentations.
Data Collection Tools: Various tools assist with data collection. Web scraping tools extract data from websites. Survey tools collect data from customers, employees, and stakeholders so companies can determine satisfaction rates. Data entry tools input data into a centralized location to ensure accuracy.

Is Coding Necessary?

Coding is not always necessary for reading big data, but it can be a valuable tool. It can help collect and organize data, develop modeling, and automate tasks.

Affordable tools like Microsoft Power BI and Tableau allow employees to experiment with coding so they can make data more accessible. They also provide chart and graph creation tools that assist with visual analysis.

Python, Anaconda, and R are free tools employees can explore to familiarize themselves with coding processes.

Visual Analytics

Visual analytics like charts, graphs, and KPI analyses gather data in one place so CIOs can get the big picture. They allow leaders to digest large amounts of data and determine the best paths moving forward.

CIOs can utilize visual analytics in various contexts. They can measure employee productivity, financial aspects of the business, and other crucial metrics. They can help leaders identify weaknesses and employ necessary improvements.

Other Analysis Techniques

Businesses can also integrate the following strategies to gain data insights.

Data modeling: Data modeling uses statistical tools to identify patterns and trends.
Predictive modeling: Predictive modeling uses historical data to forecast possible outcomes.
Data mining: Data mining hunts through large data sets to identify patterns and anomalies and draw connections.

Putting Big Data to Use

Harnessing big data is the first step. Once properly stored and characterized, leaders must put data to practical use. Applications will vary depending on industry and company needs and may include the following:

Fraud Detection: Systems can compare historical and current data to recognize anomalies and suspicious activities that may indicate fraud. Leaders can use this information to identify vulnerabilities and make improvements.
Improved Marketing: Data provides insights into customer behaviors and evolving trends. Companies can use this data to develop marketing campaigns that will appeal to their consumer base.
Identifying Growth Opportunities: Data insights into customer behavior and trends can help companies identify growth opportunities. They can use these insights to develop suitable products and expand into new markets.

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