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.
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:
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.
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 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.
Data analytics has many use cases across industries. Some of the common uses include:
These various insights can enable business leaders to take data-driven decisions rather than rely merely on gut feel to accelerate growth and profitability.
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.
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:
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:
Data analysis can help business operators assess the efficiencies and inefficiencies in their processes and go to the root of the matter by:
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:
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.
Some of the most popular tools for data analytics include the following:
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:
Jelvix | JCommerce | Beyond the Arc | Trianz| Indium Software | cBEYONDdata | Kavi Global | GetInData | Clariba| Visual BI Solutions
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.
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:
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.
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.
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:
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:
By Uma Raj
By Uma Raj
By Abishek Balakumar