“Information is the oil of the 21st century, and analytics is the combustion engine.”
~ Peter Sondergaard, Gartner Research
Data fuels today’s business world. With the economic and social fallout of the COVID-19 pandemic, data analytics has become a game-changing technology that Boards of Directors and CEOs have placed as their No.1 priority, investing much time and resources into the technology.
The post-COVID period has brought new challenges and priorities for business leaders, bringing in an urgent imperative to generate business value using data and innovative approaches to leveraging analytics technologies. In short, business leaders, including CIOs and CDOs, are now looking at data analytics to strengthen business models, making organizations more nimble and capable of responding to future disruptions quickly.
This paper will give you an in-depth insight into data analytics, types, business use cases, and the latest data and analytics trends that are helping organizations grow and respond to radical uncertainties and opportunities.
What is Data Analytics?
Data analytics is the art and science of analyzing raw data to draw contextual, meaningful, and valuable conclusions from that information. In today’s highly digitized world, most of the techniques and processes of data analytics have been automated using powerful technologies like artificial intelligence and machine learning that work over raw data for easier human consumption.
Today, any data can be subjected to analysis using AI and other technologies to reveal trends and metrics that help optimize business operations. Such valuable insights may otherwise be lost in the mass of structured and unstructured data generated by organizations every day.
A simple example could be manufacturing companies recording the runtime, downtime, and work queue for different machines at the factory to derive insights, help plan workloads better, and allow machines to operate at their maximum capacity.
Big Data, characterized by high volume, great variety, and high velocity, has been used by organizations to derive business intelligence for the past few years. With more technology now appearing on the market to assist us in accessing big data, the market is growing at an astronomical rate, set to reach a value of $103 billion by 2027.
However, today’s organizations no longer distinguish between the efforts to manage and capitalize upon big or non-big data.
In fact, everything is data for them. Leading analyst firm Gartner recognizes the transition from big to small and wide data as one of today’s top data and analytics trends.
Having defined what data analytics is, we’ll next move on to the types of data analytics and how they can help improve business processes, outcomes, and decisions.
Types of Data Analytics
Data-driven decision-making implies using big or non-big data to enhance decision-making processes.
AI analytics is a subset of business intelligence that leverages machine learning techniques to find new patterns, discover insights, and unearth relationships in the data. In short, artificial intelligence techniques take up the burden and automate most of the work that a data analyst would typically perform and even augment the speed and granularity of the data that can be analyzed.
Decision-making models majorly capitalize upon prescriptive analytical techniques to generate outputs that specify the actions. However, there are other core analytics techniques, and we’ll be studying each of these below.
Predictive analytics typically plays with probabilities to forecast a series of outcomes overtime or bring forth uncertainties related to multiple possible outcomes (simulation). It tells us what is likely to happen but does not suggest what should be done.
Predictive analytics leverages regression analysis, forecasting, predictive modelling, multivariate statistics, and pattern matching techniques.
2. Prescriptive analytics
Prescriptive analytics suggests the best way to achieve or influence an outcome, helping drive action. Predictive analytics works best with prescriptive analytics, drawing on predictive insights to comprehend what can be done or how particular outcomes can be influenced. Combining the two is usually the first step in finding solutions to business challenges.
Prescriptive analytics encompasses rule-based approaches (leveraging known knowledge in a structured manner) and optimization techniques to discover optimal outcomes within constraints and generate feasible, action-oriented plans. Prescriptive analytics uses recommendation engines, graph analysis, simulation, and complex event processing techniques.
3. Descriptive analytics
Descriptive analytics relies on business intelligence (BI) tools, data visualization, and dashboards to answer questions like “What happened?” or “What is happening?” The raw material procurement department may want answers to simple questions like “What did we spend on “Item A” last month?” and “Who are our biggest vendors for commodity X?”
4. Diagnostic analytics
Diagnostic analytics necessitates a more detailed drill-down and comprehensive data mining abilities to answer, “Why did “Event X occur?” For example, sales leaders can capitalize upon diagnostics to identify the behaviours of sellers who are on track to meet their quotas.
For CIOs and CDOs, understanding the nitty-gritty of these analytics techniques will be critical to comprehending the infrastructure, roles, and capabilities of their respective organizations will need to be entirely data-driven.
Data Analytics: On-premise vs. Cloud
By 2025, 463 exabytes of data will be created each day, the equivalent to around 212,765,957 DVDs.
That is where cloud analytics comes into the picture. While similar to on-premise analytics, cloud analytics stores and analyzes data in the cloud to draw out actionable insights.
While cloud and on-premise analytics involve advanced machine learning algorithms applied to voluminous data collections to produce meaningful insights and conclusions, cloud analytics is generally more efficient than the latter.
And why so? — On-premise analytics requires businesses to purchase and maintain expensive data centres. Even though on-premises analytics solutions give companies control over the privacy and security of internal data, the vast volumes of structured and unstructured data produced each day make them difficult and expensive to scale.
On the other hand, cloud analytics’s scalability, service model, and cost savings structure make it a better option from a cost optimization perspective.
Since businesses generate a hefty data load, cloud analytics tools come in handy. They are particularly efficient for processing massive data sets, creating meaningful insights on demand and in easily digestible formats, resulting in more streamlined user experiences.
Role of Data and Analytics in Business
Data and analytics are soul food for modern businesses. Data analytics can accurately predict outcomes for all types of decisions. Still, it can also help business leaders discover new questions, solutions, and opportunities — even those they had not even considered.
Progressive organizations use data to make smarter business decisions. Moreover, in today’s fast-paced digital environment, data analytics also catalyzes more agile, flexible, and faster decisions related to the organization’s digital strategy.
Data-driven decisions not only drive prudent action, they equally determine when it may be the wisest decision not to act. Progressive organizations are leveraging data analytics to create a data-driven enterprise, quantify business outcomes, and foster data-rich decision-making.
While organizations can access a lot more data than ever before, different industries can leverage data analytics in distinct ways to deliver better experiences.
Data Analytics Use Cases in Different Industries
Data analytics can help analyze structured (year, model, and make) and multi-structured equipment data (sensor data, engine temperature, error messages, log entries, etc.). Such analysis can help enhance equipment uptime and deploy cost-effective maintenance. Manufacturing organizations can predict equipment failure and forecast the remaining optimal life of systems and components to ensure they perform within set specifications.
Analyzing and assessing big data within factories and manufacturing units can help organizations improve production processes, respond to customer feedback proactively, and anticipate future demands for materials and stock. Enhanced operational efficiency ultimately leads to greater profitability.
Production optimization plays a significant role in reducing costs and increasing manufacturing revenue. Understanding the flow of items through production lines can reveal what steps can increase production time and which areas lead to unnecessary delays.
Improving patient care
Analyzing big and non-big data can help hospitals and other healthcare organizations get a 360-degree view of patient care, including patient history and past and current treatments. The patient seeks medical care in different departments, helping improve patient care dramatically at lower costs.
Detecting claims fraud
Every healthcare claim comes with hundreds of associated reports in different formats, making it extremely challenging to verify the accuracy of claims and prevent fraud. AI-enabled data analysis helps healthcare organizations detect potential fraud by identifying anomalous activity and patterns that flag certain behaviours.
AI-driven data analytics can help healthcare researchers identify disease genes and biomarkers that pinpoint future health issues in patients. Consequently, healthcare personnel can design personalized treatment plans to prevent or mitigate disease.
Intelligent product development
Data analytics can help with intelligent product development. Data analytics can help build predictive models and prototypes for future offerings by identifying product attributes that gained popularity and commercial success. Moreover, digging deeper into data from focus groups, test markets, early store rollouts, and social media can help launch new products.
Enhancing customer experience
Like patient care, data analytics can also provide a 360-degree view of customers. By gathering data from customer purchases, and customer behaviour, including social media interactions, web visits, and other company interactions, companies can proactively offer greater customer value, increase CSAT, reduce churn, and maximize revenue.
Ensuring lifetime customer value
Retail organizations need to realize that some customers may be more valuable than others. They will be willing to spend the most money, over the most prolonged period of time, in the most consistent manner.
And data analytics is the best way to pinpoint such customers. AI-driven information analytics provide valuable insights into customer behaviour and spending patterns. Once a company knows its best customers, it can allot devoted sales team executives to them and target special sales offers.
Product propensity analytics studies the combined data on purchasing activities and customers’ online behaviour (metrics from social media and e-commerce platforms). It then correlates this data to gauge the effectiveness of different campaigns and social media channels concerning the company’s products and services. The company will get to know which customers are more likely to buy your products and services and the channels that are most likely to reach those customers, allowing them to maximize revenue.
Retailers must know the pricing potential of their markets and the true spending potential of their customers. End-to-end analysis of profit and margin data can pinpoint pricing improvement opportunities and areas where profits may be leaking.
A significant use case of data analytics common to all customer-facing industries is sentiment analysis. Today customers live on the internet. Whether it’s social media sites like Twitter, Instagram, or Facebook, or feedback shared on the company’s web portal or app, brands are being talked about everywhere. But It’s very challenging for companies to be everywhere at all times.
Hence, capturing and reviewing everything said about your organization is impossible.
However, by leveraging web search and crawling tools on customer feedback and posts, companies can create analytics that provides a sense of their reputation within key markets and demographics and delivers proactive recommendations to deal with it.
To improve the overall merchandising experience and encourage customers to complete purchases, retailers can analyze data from mobile apps, websites, in-store purchases, and geolocations.
Banks and other financial services
Prevention of fraud
The banking and financial services industry is constantly up against an entire team of expert hackers to ensure security and data privacy. In a constantly evolving security and compliance landscape, machine learning-driven data analytics can help the BFSI industry identify patterns that are indicative of fraud and aggregate enormous volumes of financial information to streamline regulatory reporting and compliance.
Driving innovation in products and services
Data analytics makes the interdependencies between customers, processes, institutions, and markets more apparent. A better understanding of market trends, regulatory requirements, and customer needs can help organizations innovate new products and services.
Anti-money laundering laws require banks and financial services institutions to show proof of due diligence and hand over suspicious activity reports. In this regard, AI-driven big data analytics can help companies detect anomalous activity and identify potential fraud patterns.
Oil and Gas
Oil and gas companies often lack complete visibility into equipment conditions, especially in deep-water and remote off-shore locations. The industry leverages IoT sensors to monitor and track the performance and health of machinery, oil wells, and equipment operations.
Data analytics can help uncover valuable insights from this information so that companies can predict the remaining optimal life of their systems and components, ensuring assets operate at optimum efficiency.
AI-driven analytics can unearth valuable information from data generated by seismic monitors at new drilling and production sites. Such data can find new oil and gas sources by identifying traces that might have been overlooked before.
Optimizing oil production
Using predictive analytics and unstructured sensor and historical data, companies can optimize oil well production, understand usage rates, and determine the reason behind differences in actual good output and the predicted numbers.
- Network capacity optimization
Optimal network performance is key to a telecom’s success. Network usage analytics can help telecoms list areas with excess capacity and reroute bandwidth. Moreover, data analytics can help plan network infrastructure investments and design services that meet customer demands.
Avoiding customer churn
Telecom companies can leverage analytics to study service quality, network performance, and convenience and draw conclusions about overall customer satisfaction. They can also set up alerts when customers are at risk of churning and proactively send personalized offers and activate retention campaigns to lure back customers. This way, telecoms can maintain customer loyalty and avoid losing revenue to competitors.
Designing personalized customer offerings
An improved understanding of customer behaviour, intention, and preferences enables companies to tailor new services and features to different customer segments.
The Future of Data Analytics
Indeed, business leaders have learned valuable lessons in the past two years. The most significant of them is that every digital business moment, big or small, is powered by data analytics.
Boards of Directors and CEOs are investing much in AI-driven technologies to gain repeatable and sustainable value from data.
Siloed data, a focus on data for its sake, misalignment of stakeholder expectations and outcomes, and the assumption that acquiring the right technologies for analysis of valuable information will suffice are some colossal challenges business leaders will have to face to move forward strategically amid continued disruption.
C-suite executives must realize that data analytics is no longer an alone strategy merely about dashboards and reports. Today, a business strategy infused with data and analytics is needed to make smarter business decisions.
Changing the perception of data analytics as merely an IT service that runs reports on request is vital to embedding it as a core business strategy central to business functioning.
Here, the role of the Chief Data Officer (CDO) comes to the forefront. CDOs are responsible for orchestrating their data assets to guide decision-makers using valuable data insights. Gartner’s research suggests that by 2022, 30% of CDOs will partner with their CFO to formally value the organization’s information assets for improved information management and benefits.
Gartner predicts that if chief data officers (CDOs) are involved in setting goals and strategies, they can increase the consistent production of business value by 2.6X.
Moreover, creating a conducive environment wherein data literacy takes precedence is essential for driving meaningful organizational change. By 2023, data literacy will become an explicit and necessary driver of business value, demonstrated by its formal inclusion in over 80% of data and analytics strategies and change management programs.
Driving meaningful change management by creating awareness and spreading positive word of mouth about how outcome-driven data analytics can deliver the most significant business impact is imperative.
Below we discuss the leading data analytics trends that can drive radical change in economic, market, and technology dynamics, helping organizations anticipate and shift and respond to the disruption in the coming years.
- Data fabric, a single environment consisting of a unified architecture, services, or technologies for managing data, is the foundation of data analytics.
- Augmented analytics, a leading business analytics trend, uses natural language processing, artificial intelligence, and machine learning to automate data analytics and insight discovery. Augmented analytics enhances business outcomes and makes business processes relatively easier, from data preparation to data processing, to derive meaningful conclusions.
- As opposed to big data, small and comprehensive data are helping companies enhance contextual awareness and augment decisions on complex and scarce data use cases.
- IoT, or the Internet of Things, contributes a significant chunk to the development of today’s Big Data and data analytics landscape. Research by IDC says that the number of IoT devices connected to the web could rise to 41.6 billion by 2025. Moreover, 127 devices connected to the internet every second. These devices produce five quintillion bytes of data a day, which is a tremendous amount of information.
- Replacing predefined static dashboards and manual data exploration restricted to data analysts or citizen data scientists, conversational, mobile, and more dynamically generated insights, will be the future of data analytics.
- XOps, a combination of AIOps, MLOps, DevOps, BizDevOps, and GitOps, will ensure reusability, reliability, and repeatability and operationalize data and analytics to drive business value.
According to a study, there is so much online data that it would take 181 million years to download everything. However, today’s businesses need to leverage only the tiniest portion of this data. This is where data analytics comes into being.
Today, AI and analytics are the no.1 and no.2 priorities of Boards of Directors and C-suite executives.
Creating a data-driven culture that prioritizes data literacy and change management will help embed data analytics in business strategy and build an outcome-driven organization.
Indeed, data analytics is not just a trending topic, nor is it a passing buzzword. Instead, it is one of today’s and tomorrow’s most critical strategic opportunities that will be used to control, improve, and redefine every industry.