The Ultimate Guide to Data Analytics

Blog: Indium Software - Big Data

Introduction

Access to High Performance Computing (HPC) systems that can process data fast and the availability of open-source frameworks such as Hadoop are enabling businesses to leverage big data analytics to accelerate growth. They can get insights across functions, right from customer behavior to operational issues, that can help businesses formulate strategies based on data rather than mere gut feeling.

This increasing demand for data analytics has spurred the growth of the global data analytics market at a CAGR of 28.9% from USD 22,998.8 million in 2019 to USD 132,903.8 million by 2026. Businesses use scientific models to organize raw data, create data models and spot trends from data sets to classify and cluster data and forecast future trends.

Forrester calls businesses that can convert insights into business decisions as an insights-driven business (IDB) and states that even those that are in the early stages of being able to leverage insights are 2.8x more likely to experience double-digit growth year-on-year.

What Data Analytics Can Do

Technologies have enabled businesses to expand their reach beyond geographical boundaries with greater ease. But this globalization has also increased competition, the need for managing suppliers from different geographies, improving operational efficiency and reducing costs to remain competitive.

Data analytics can help businesses in many ways:

Increase Customer Delight: More than customer-centric or customer-driven, today businesses have to be customer-obsessed to be able to succeed. The customers are spoilt for choice and can buy locally, from a store nearby, or opt for a global supplier through online ordering. As a result, the traditional idea of loyalty is undergoing a dramatic change with customer seeking suppliers who can fulfill their needs quickly and effectively. This requires businesses to go beyond merely acquiring customers but also find ways to understand their needs better and proactively reach out to them in multiple ways to keep them happy. Data analytics can make this happen by enabling businesses to identify customer buying trends, personalize messaging, customize offers and improve product and service delivery.

Mitigate Risk & Fraud: Data analytics can enable detecting fraudulent activity by sending automatic alerts in case of any threat to the systems. This can help improve the overall organizational data security while also facilitating identifying and tracking perpetrators using statistical models and big data methodologies. With transparent and efficient fraud reporting mechanisms in addition to data management, organizations can improve fraud risk management processes.

Enhance Product Design: The product design team needs inputs from customers as well as the market to be able to arrive at a strategic roadmap for product design, innovation, new features, and meaningful services. Data analytics can help with sentiment analysis as well as draw insights from external factors that influence purchase decisions. This could be related to climate, political environment, festival seasons and sometimes even the demand for related products as well as arrive at the pricing strategy.

Improve Operational Efficiency: A lot of data is generated these days by the different processes within an organization and across the supply chain. Running analytics on these process data can help to identify inefficiencies and implement steps to improve them. This can greatly enhance productivity and profitability.

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Is Data Analytics Hard?

Though the benefits of data analytics are many, the challenges to effective data analytics is a sum of many steps, all of which need to be done right for the organization to benefit from it.

Collecting the Right Data: It first requires the right kind of date. Though large volumes of data are available today, only relevant data can provide the right insights to achieve the business goals. Therefore, the first step is to identify the goal and then identify and collect relevant data. In today’s context, this is not restricted to structured data also but also unstructured such as textual, images, audio and video data.
Processing It: The data collected is in raw format. This needs to be cleaned, processed and stored in a way that makes retrieval and analytics easy. It requires the team to have skills such as statistics, data cleaning, exploratory data analysis, and visualization. Such skills are hard to find and so may make data analytics seem difficult.
Scalability: Often businesses can achieve successful pilots but unable to operationalize due to scalability challenges. Issues with data integration and poorly defined goals along with data silos and lack of futuristic planning can also contribute to failure.
Tech Generation Gap: While businesses are raring to go for real-time data analytics, their technology may be from a previous generation and enable only batch processing. This can render the insights drawn meaningless due to the data being dated by the time the processing is done.

There are many more such scenarios that could be related to the data, the analytics process or the technology that can make data analytics seem hard. Developing the expertise or partnering with experts, setting clear goals, sanitizing data and so on are some of the ways in which these limitations can be overcome.

Data Analytics vs Business Intelligence

Data analytics and Business Intelligence have common processes – data collection, report generation, analytics, and insights generation. Both provide insight into areas where businesses need improvement or are not operating at optimum efficiency. This insight enables businesses to effect improvements and increase efficiency across operational areas.

However, business intelligence reviews a business’s current operations through aggregation, visualization, and analysis, improving decision making and providing insights on how it is performing in the present. It can help with improving operational efficiency.

Data analytics on the other hand helps with predicting the future by integrating real-time data with historical data for identifying patterns and forecasting future trends. This helps with developing a roadmap for the business, its products and services.

There are many BI tools available in the market and most empower business users with self-service. That is, the users do not need technical skills but can run the tools to get the insights they are looking for. Data analytics, on the other hand, needs greater skills as it requires developing and using algorithms to run on a larger volume of data.

Where is Data Analytics Used

Data analytics has many use cases across industries. Some of the common uses include:

Understanding Customer Behavior: Improving customer conversion rates is one of the keys to experiencing accelerated growth. Data analytics enables this by providing data-driven insights that can help understand customer needs and improve products and services, improve pricing strategy as well as personalize campaigns to increase sales, reduce customer acquisition costs and lower churn.

Customer Profiling: Different customers have different needs and the messaging needs to be designed accordingly. Businesses need to know their customers, from where they come, what are some of the other products they are interested in, which can help with upselling and cross-selling. This can also help with merchandising and creating the right product mix in the case of a retail store, both online and offline. This can also help with employee training to be able to serve customers better and increase the value of the transactions.

Predictive Maintenance: Equipment and machinery in a manufacturing set up are a capital expenditure. To increase the life of the products, IoT based equipment help assess machine health and plan maintenance to avoid unscheduled downtimes that can affect production.

Inventory Management: Keeping costs low by avoiding wastage and just-in-time manufacturing can help businesses improve inventory management and avoid waste.

Quality and Compliance: Quality assurance is essential not only for brand reputation but also to meet regulatory requirements. Data analytics can help identify gaps and devise strategies to ensure quality. It can help prevent incidents and also predict future potential events. It can help with root cause analysis as well as taking corrective and preventive action to prevent the recurrence of incidents. Data analytics can also help with identifying risks and take timely, mitigative action.

These various insights can enable business leaders to take data-driven decisions rather than rely merely on gut feel to accelerate growth and profitability.

Why Is Data Analytics Important for Problem-Solving?

To remain competitive and experience growth, businesses need to understand their customers, their operations and their challenges. While organizations have always worked with data and tried to analyze them for making business decisions, the data has been siloed. Also, technology did not support access to real-time data, because of which the effectiveness of the analysis was limited.

In today’s world of digital transformation, access to real-time data and enterprise-wide view of processes can help businesses leverage data analytics for solving problems in real-time and improve their effectiveness. provide them with visibility into the different processes and by running analytics on this data, they can identify areas that need optimization for greater efficiency and improvement.

They can identify, assess and prioritize risks so that the more critical ones can be mitigated on priority. This can help them channelize their resources more fruitfully instead of trying to solve all problems at the same time and being inefficient as a result.

In these times when cost efficiency is very critical to improve profitability, resource optimization is also critical for businesses. Data analytics throws light on how resources are being used and how they can be utilized better to improve productivity and efficiency.

Business models need to keep changing to keep pace with the changing market dynamics. The earlier concept of 5-year plans no longer hold good and need to be constantly fine-tuned and updated to keep pace. This is possible only with real-time analytics that can provide insights into the changing trends and forecast future trends to enable businesses to decide the direction of their business.

Data analytics can also help identify new opportunities by highlighting strengths, weaknesses, opportunities and threats. This can help generate new revenue streams, retire fruitless pursuits and enable better focus.

While it is fairly easy to spot glaring mistakes and correct them, often the small but significant mistakes may get overlooked. They may add up to hurt the business and need to be addressed to improve profitability or efficiency. Data analytics can help throw light on these problems and solve them in a timely manner.

Of course, as seen earlier, improving product design, customer service, operational efficiency are some of the other ways in which data analytics can help with problem solving.

Can Data Analytics be Automated?

Artificial intelligence and machine learning have become all-pervading, doing monotonous, routine tasks while freeing up resources to do more value-added activities.

In data analytics too, several routine jobs can be automated using algorithms and machine learning. Right from data discovery to preparing, streaming, processing and aggregating data up to organizing, processing and analyzing while ensuring data security and data privacy can be achieved through automation. It can enable proper and efficient data management without information loss and at lower computational costs.

By automating, data scientists can speed up analytics as machines can perform data processing faster, accessing different sources quickly and more efficiently. The accuracy of the data can also be ensured using machine learning, which can handle large volumes of data quickly.

It can enable self-service for business users, who don’t have to depend on the IT teams to run algorithms and generate reports needed for analytics. This frees up the IT teams to focus on their core tasks while business users can create visualizations based on their needs. Creating dashboards and visualizations based on user needs is also simpler.

For the automation to be effective, ensure:

Clarity of goals and expectations to facilitate better collaboration between the stakeholders such as the marketing, operations, and human resources teams.
Establish metrics to measure the effectiveness of the automated processes in achieving the established goals.
Choose tools based on the needs of the business and the ease of use to truly derive the benefits of automation.
Follow a Plan, Develop, Check and Act cycle before operationalizing automation to prevent unpleasant surprises later.

How is Data Analytics Transforming Business?

A McKinsey report states that a well-executed data analytics project can result in 15 to 20 percent growth in revenue due to improved yield management, optimization of operations, deep customer insights, personalization, and forecasting trends. There is also a cost reduction because of automation, lowered reporting costs and reduction in data storage.

Businesses are also able to capture the value better due to the availability of In-memory computing, the use of machine learning for data cleansing and data management, and the availability of external data sets.

This enables businesses to achieve digital speed, focus on innovation, make faster and bolder decisions that are data-driven and be customer obsessed.

Data analytics is one of the key aspects of digitization where all the data an enterprise now has can be effectively leveraged to understand trends and predict the future. Some of the key ways in which it is facilitating business transformation include:

Increased productivity by identifying areas where efficiencies can be improved
Informed decision-making using data for support rather than follow a gut feeling
Creation of new revenue streams by identifying potential opportunities and market needs
Improving competitiveness by enhancing service and product capabilities
Responding to customer needs better through a deeper understanding of their expectations
Mitigation of risks and fraud through timely detection of threats and enabling timely response as well as ensuring the security

Can Data Analytics Improve the Evaluation Process?

Data analysis can help business operators assess the efficiencies and inefficiencies in their processes and go to the root of the matter by:

Generating reports on what caused the problem
Diagnose the reason for its occurrence
Predict the possibility of its recurring in the future
Recommend solutions

Data analytics can be used to evaluate processes across functions, the performance of the teams involved, the nature of information exchanged and other components of the process. This can help identify areas of strengths and weaknesses including:

Causes for systematic delays
Finding the correct process as well as the right way of implementing it
Ensuring maximum potential capacity
Identifying areas of improvement

How Can Data Analytics Help Executive Management?

Businesses have always relied on data to make decisions. But in the absence of real-time data and advanced analytics techniques, the decisions would often be dated and ineffective and some gut feel would be required to improve on strategies. The siloed nature of systems also proved a challenge to get an enterprise-wide view for effective decision making.

Digital technologies and data analytics in today’s world empower the management with information that can help them with decision making. With data analytics, they can also see the impact of their decisions and change where needed. Data analytics can also help them monitor the progress of the projects and effect a mid-course correction where needed.

Not just in the headquarters, data analytics also help with remote management of offsite locations, the supply chain and marketing activities. It can help with resource optimization, asset maximization and reduce wastage.

What are the Best Data Analytics Tools in the market?

Some of the most popular tools for data analytics include the following:

R: Popular for statistics and data modeling, it is a flexible tool compatible with many different platforms such as Unix, Windows and MacOS. It enables working with and presenting the data in different ways and its 11,556 packages facilitate browsing the packages by categories.
Tableau Public: A free software, it enables data visualizations, maps, and dashboards by connecting any data source including Data Warehouse, Microsoft Excel or web-based data, to create real-time updates that can be shared with the client, on the web or the social media.
Python: An free, open-source, object-oriented scripting language, it is easy to read, write, and maintain and supports structured and functional programming methods.
SAS: This is a programming environment and language that can analyze data from any sources and is popular for customer profiling, predicting behaviors and optimizing communications.
Apache Spark: Built for data science it enables quick large-scale data processing and execution of applications in Hadoop clusters. The progressive set of machine algorithms available in its MLlib library allows the use of repetitive data science techniques such as Classification, Regression, Collaborative Filtering, and Clustering.
Excel: From Microsoft, this basic but popular tool is used widely for analytics across all industries and is especially important when using client’s internal data. It can perform complex tasks such as summarizing data with a preview of pivot tables for filtering as required by the client. Its advanced business analytics option enables modelling capabilities with prebuilt options such as time grouping, automatic relationship detection or creation of DAX measures.
RapidMiner: This can work with any type of data source and generate analytics as needed.
KNIME: A leading open-source, reporting, and integrated analytics tool, it uses visual programming to analyze and model data. A modular data-pipelining concept is used to integrate the different components for data mining and machine learning.
QlikView: Its greatest asset is its in-memory data processing, enabling quick execution as well as storing the data in the report itself.
Splunk: It allows you to search through all kinds of data and gives the freedom to run interesting statistical analysis as well as present the results in varied formats.

Top Data Analytics Services Companies and choosing the right partner

Several data analytics companies around the world specialize in various segments. For instance, marketing data analytics requires specialized expertise when compared to say, supply chain analytics or quality analytics. However, at a fundamental level, strong data analytics services companies have expertise in data engineering and data analytics.

Data engineering requires expertise in big data implementation, data migration, data warehousing, data lakes and data virtualization. Data analytics requires a team of data analytics, data science experts and technology professionals who specialize in advanced analytics, text analytics, data visualization, IoT-based data analytics and so on. The key is to choose your data analytics partner with a documented vendor or partner selection criteria.

Indium Software is among the Top 15 specialized data analytics companies according to Clutch.co. We are part of these Leaders Matrix and the list of companies on the list include the following companies:

What about a career in Data Analytics?

Data analytics has become critical for businesses to improve competitiveness, operational efficiency and product and service delivery. Data analytics requires the coming together of data, tools as well as analytical skills, which is rare. Therefore, there is a big demand for data analysts and there are not enough in the field. As a result, data analysts can earn well even at the entry-level.

Roles and Responsibilities of a Data Analytics Professional

The data analyst is responsible for analyzing data for different purposes and creating dashboards that can provide users with insights to facilitate informed decision-making.

For this purpose, they need to wade through tons of data to extract the right kind of information that can help with identifying trends, predict the future and improve decision making. Their roles start even before the analytics stage and begins with:

Recommending methods of acquiring and managing data
Collecting and interpreting data
Interpreting results
Spotting trends
Sharing findings with relevant business users

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In simple terms, a data analyst is a person who breaks down complex insights to derive some kind of meaning. These insights need to answer questions necessary to make business decisions. This will require the data analyst to have or acquire some domain knowledge to understand the context and improve the efficacy of the analysis. Statistical skills are important to create suitable charts and represent data visually for easier understanding by the users.

Well, Is Data Analytics the Future of Everything?

Data analytics has been evolving. And businesses have been evolving too, their data analytics projects becoming more mature with time. From descriptive, to understand the past, it matured to reach the next stage of diagnosis to identify the cause.

The next stage is discovery, of learning for improving, then to predictive, and, finally, to prescriptive analytics where mature organizations can rely on it for making the best decisions. Currently, a large number of organizations are still in the diagnostic and discovery stages. As the future unfolds, the role of analytics will keep increasing till businesses can leverage the true purpose of data analytics and build from there.

Conclusion

The access to data and data analytics tools has empowered businesses to improve their overall decision-making process. It is now based on facts rather than only gut feel, though intuition and experience will continue to play a role. However, the journey to scale up data analytics projects from pilot to across the enterprise will require businesses to:

First set the goals
Identify what data they need
How and where it will be stored
Have the right tools and skillsets to operationalize it
Assess the impact and fine tune based on changing needs
Move up from descriptive to prescriptive models to truly benefit

The demand for capable analysts and data scientists is going to increase, though automation can reduce the overall cost of resources. A well-implemented project can help businesses accelerate growth and improve efficiency across functions.

Working with partners such as Indium Software, who have a team of data experts and can manage data analytics projects end-to-end will prove valuable for businesses. This will enable them to continue focusing their resources on core business activities while allowing the partner to guide and implement analytics projects.

To know more about how Indium can help your enterprise with your data analytics needs or to join a wonderful team of fellow data analytics professionals, contact us now:

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