Why Data Analytics Technology Matters

We hear about data analytics all the time. But, what exactly is it? We can define data analytics as the science of examining raw data with the aim of drawing some hypotheses about the information contained therein, using various specialized systems.

Data analytics encompasses many different tools and technologies, all of which help enterprises process business data and use the results to make significant decisions for strategic management and achievement of their business goals.

In the recent past, there have been many studies that have shown that data-driven business decisions yield far more superior results than decisions based on leadership instincts. This has prompted corporate leaders to promote and encourage the use of cutting-edge data analysis technologies. Today, organizations are using specialized systems and high-impact software for data analytics.

Simply put, data analytics initiatives can help businesses increase revenues, improve operational efficiency, optimize sales and marketing efforts and refine customer service. By making informed, data-driven decisions, organizations can respond more quickly to emerging market trends and gain an advantage over their competitors.

Given the ever evolving technology landscape, data analytics has assumed great importance for different kinds of businesses.

Broadly speaking, we can classify data analytics by two levels: exploratory data analytics (EDA) and confirmatory data analytics (CDA). Exploratory data analytics simply refers to ‘exploring’ relationships or patterns in data sets and defining the characteristics of the data. EDA often involves visual or graphical methods to represent data, such as scatter plots, histograms, and multi-vari charts. EDA can be used to suggest tools for further data analysis, as well as to provide a basis for further collection of data through surveys or questionnaires.

Confirmatory data analysis or CDA, on the other hand, refers to statistical analysis which aims to confirm a pre-defined hypothesis. It uses techniques such as regression analysis or variance analysis to challenge assumptions, question if hypotheses about data sets are true or false, and arrive at estimates with a certain amount of precision.

In practical application, EDA and CDA data scientists go back and forth between the two. For example, using exploratory data first to come up with a hypothesis about the relationship between two data sets and then using confirmatory data to evaluate whether the hypothesis is true or not, and then switching back to exploratory analysis to explore the data sets for further relationships.

Many open source as well as proprietary tools are available to help data scientists with exploratory analysis – such as R, Rattle GUI, Orange, and Tableau, which are some of the more commonly used ones. Confirmatory analysis can also be performed using environments like R and other commercial software like Mplus or AMOS.

Another way of looking at data analytics is to classify it as either quantitative or qualitative analysis. Very simply put, quantitative analytics refers to data which is numerical – that is, can be expressed as a number. For example, the number of leads generated by a marketing campaign. 

Qualitative data, on the other hand, is more interpretative and is used to provide subjective analysis in situations where hard numbers are not feasible or appropriate. It focuses on non-numerical data, such as text, images, video. For example, video analytics has developed the ability to measure the frequency at which a particular human face (i.e. recurring, recognizable pattern) occurs. This may help in alerting security authorities at different points in time to tap in and take appropriate action. Or, as another example: many companies ask customers how or where they reached them – whether through a search engine, through a reference from family or a friend, or through email, etc. 

It is vital to understand which situations warrant which type of analysis and implement it correctly to derive valuable insights out of data.

Business Analytics: Analyzing business data to support strategic decision-making

Organizations today need to look at past performance data, competitive data and various other sources of data to get actionable insights. Business intelligence or BI, as it is popularly known, refers to strategies and technologies used by organizations to analyze business data. BI technologies include data mining, predictive analysis and benchmarking, as well as data visualization and dashboard reporting. A combination of these tools and techniques provide the organization with a comprehensive view of the business’s operational performance and can bring forth insights into new business opportunities or opportunities for growth in certain aspects of the business through increased efficiency or better performance.

While in earlier days BI applications tracking KPIs, behavioral trends and growth statistics were used mainly by IT staff, now there are self-service BI tools available, which make it easy for non-technical staff — even corporate executives — to generate different BI reports and dashboards to visualize and understand different aspects of business data.

Thus, BI helps leadership attain insights that can drive change within the organization and transform the business into a data-driven mindset and culture.

Artificial Intelligence and Machine Learning (AI/ML): The New Face of Data Analytics

With the growth of automation and the advent of IoT, the amount of data generated is increasing exponentially. In the near future, BI analysts will begin to handle more routine tasks related to data processing, while expert-level data scientists will be instrumental in key areas, such as understanding business needs and providing strategic insights. Accordingly, they will need to focus increasingly on the machine learning operations process or MLOps.

A new kind of data-driven organization is emerging, which is driven by algorithms rather than relying on traditional business processes. In this data ecosystem, IT professionals design the AI architecture and the software makes the algorithms work. Automated systems deliver the outcome and human interventions are minimized, while the automation engine assumes the core of the firm. In fact, it is precisely this scenario that has led many organizations to future-proof their business landscape with investments into data analytics and AI/ML. In this scenario, an organization’s ability to understand and interpret their business data will determine how much of a competitive advantage they can claim.

Once an organization adopts a data-driven approach at an operational level, a ‘culture of data’ begins to take root at the ground level. Operational workers across different functions in the organization start to adopt a data-oriented mindset and, over time, the organization transforms itself through what is referred to as a process of digital transformation — a process that has earned so much hype but has been so difficult for organizations to attain. Thus, another aspect of the importance of data analytics is its role in transforming an organization’s culture.  

Big Data: Is it really big and important? 

By now, we can see that what is important is not how much data a company has, but what it does with the data. In fact, the larger the volume of data that a company has, the more vital it becomes to use advanced forms of analytics on such ‘big data’. Tools like Hadoop, coupled with cloud-based analytics, can deliver huge cost savings, a significant reduction in processing time, recognize market trends and patterns, analyze market sentiment about a company’s product or service, and provide insights for innovation around products and services.

One may argue that big data and AI/ML are all fine for large corporations, but what about smaller organizations? Is data analytics also important for them? It certainly is! Let’s take a look at how.

Data analytics and your ‘friendly neighborhood’ small enterprise: 

Any organization in today’s world, whether big or small, collects and stores business data. For example, most companies today have a website. Webpage data is collected and analyzed using clickstream analytics which can throw light on trends and patterns in the path taken by a website visitor as he or she navigates through the website. Data points such as what pages were viewed, how much time the visitor spent on each of the web pages, demographics of the visitors, and whether the visitor completed any conversion action on the website, can all be tracked using clickstream analytics.

Similarly, smaller companies that have a customer service department also have a significant amount of CRM data and should be ensuring that they use CRM Analytics to improve the efficiency and efficacy of their customer interactions and outcomes. Past data of customer interactions, queries and complaints can be a rich source of information on how call center staff needs to deal with such situations and improve customer satisfaction levels. Customer relationship management can also be enhanced by providing customer service staff with real-time and detailed information about the customer’s transaction history and service history.

Predictive analytics may be used by medium to large companies, such as banks, to analyze spending patterns and predict the likelihood of fraudulent transactions. Predictive analytics can also be used by healthcare organizations to study patient data over time and predict the likelihood of certain diseases occurring, or identify critical investigations that may be needed in the future, based on the patient’s historical medical records.

To summarize, irrespective of the size of the organization, the importance of analyzing business data cannot be underestimated. Data analytics helps organizations make better decisions, predict outcomes more accurately, improve operational efficiency, understand risk factors for their business, and improve their products or service levels.

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