Data analytics is not a new phenomenon. With vast amounts of data being generated every day, the time has come for SMEs to make the most of it. Raw data is of little use if an enterprise doesn’t know what to do with it.
Manual processing of such volumes of data is near impossible. But many small and even large organizations have been hesitant to invest in Advanced Data Analytics. They felt it was a time-consuming and cost-intensive process, without understanding how it could help their business. Data-driven business processes were not a priority.
But things are changing fast. During the last year or so, more and more enterprises realized the importance of becoming a data-driven business to survive the competition and retain the market share. Advanced data analytics, predictive analytics, descriptive analytics, etc., became prominent as the focus shifted to building an analytics-driven organization.
In the Telecom and Financial Services industry, 25% of the companies use data analytics, compared to 17% of them in 2015. The third and fourth places are held by the Technology and Healthcare industries. North America has 55% adoption, EMEA is close behind at 53%, and the Asia Pacific region shows an adoption rate of 44%.
However, there are still questions and doubts about how advanced data analytics can contribute to achieving organizational goals. Will building a data analytics team or taking the assistance of a data analytics company really help SMEs? Let us try and answer these questions. We will first start by understanding what advanced data analytics is and why it is important for every enterprise.
What is Advanced Data Analytics?
The Importance of Advanced Data Analytics
Organizational Structure for Data Analytics and Big Data
For advanced analytics to successfully contribute to the growth and development of an organization, changes have to be made to the structure, work culture, and systems within the business. Only when all the systems and processes align with each other is that the organization can achieve its goals.
You can read more about data & analytics organizational models, roles, and responsibilities on our blog about how to build the right data science team for an enterprise.
According to statista.com, the value of the software segment of Advanced Data Analytics and other Big Data Services will increase to $46 billion by 2027.
One reason organizations didn’t want to invest in advanced analytics was that they felt the insights were not accurate enough. The reason for this could have been anything. Some enterprises indeed failed to get the right kind of results from data analytics.
However, it is crucial to understand that the reports generated by advanced analytics are based on the data input. When the data analytics team enters the correct parameters to process data, the insights will naturally be accurate. In fact, using AI tools had led to an increase in the accuracy of data analytics predictions.
Cloud storage has been here for more than a while now. From only the giants like Google and Microsoft offering cloud services to private players creating cloud platforms, there has been tremendous growth in this area.
Most SMEs have migrated their business systems to the cloud to cut down operational and maintenance costs. When data is stored on the cloud, it becomes easier for employees to access the required information. This cuts down the time taken to process access requests and sharing of data through emails. Employees at every level can make faster decisions and complete the work in less time.
One way to achieve the business goals faster and with increased effectiveness is by automating the time-consuming recurring processes and tasks. This lets employees become more productive as they can complete larger amounts of work in less time.
The risk of human error is also reduced thereby, increasing the overall quality and efficiency of the business operations. Data analytics teams build business models that help SMEs adopt new technology and processes into the business systems and empower employees.
Collaborations are now an inherent part of businesses. Remote collaborations have increased during the last year due to the pandemic lockdown and restrictions. The organizational structure of business analytics allows the teams in different functionalities to work with each other and achieve common goals.
For example, the sales and marketing teams can work together to promote a product/ service, identify potential leads, and increase the conversion rate in less time. When the teams work in tandem, they will have better control over the process.
Reacting to the changes in the market is no longer enough in the current scenario. By the time you notice a change, understand it, and then proceed to work it out (either to your advantage or to control the damage), it will be too late to make an impact on the market. If your competitors have predicted the events, they would be well equipped to deal with them.
Can you let something like that affect your business in this highly competitive market? No, right? If you want to make the most of market opportunities, you have to be proactive. You need to prevent mistakes from happening rather than limit the extent of the damage caused by these mistakes.
What is the one thing that is troubling almost every organization? Data security. If your data and business systems are not protected from cybercriminals, it can be impossible to achieve your goals (especially the long-term ones).
The latest data compliance standards set by governments have put SMEs in a tricky position. Data analytics can help enterprises identify weak areas and create a multilayer security system to increase data security and data privacy. Whether it is in-house data centers or remote backup services, data security ensures that the organization can focus more on achieving its goals without worrying about security breaches and losing confidential data.
Retail, banking and insurance, and eCommerce sectors see fraudulent transactions and claims by customers and cybercriminals. By integrating data from all departments and correlating the events to create patterns, data analytics models can help detect fraudulent transactions even before they take place.
Advanced data analytics empowers enterprises to identify and forecast the trends in frauds and alert the employees in advance. This reduces the time and money businesses have to spend on uncovering the fraud and prevent it from happening again.
One of the main aims of developing organizational models for big data and analytics is to identify business opportunities in the market before competitors can take advantage of them. Be it the untapped market segment, entering new markets, reaching out to a different set of target audiences, analyzing big data will help businesses in having a better understanding of the market conditions.
When you can predict an increase or decrease in demand for a product, you will have enough time and resources to make the necessary changes to production, warehousing, and logistics. You will be in control of the situation when it finally occurs in the market.
Personalization and customization of products and services have become essential to keep customers happy. However, it is not as easy as it sounds. Offering personalized suggestions and products includes working on big data and structured data in real-time.
Advanced data analytics organization structure should be highly responsive to keep up with the changing requirements and opinions of customers in the market. By understanding what makes customers happy, businesses can achieve their goals of increasing sales and return on investment.
We know that happy customers result in more sales, thus increasing the profits of a business. Advanced data analytics can be used to design, control, and optimize business operations that directly impact customer experience.
Be it in the sales and marketing department, inventory and supply chain management, or the customer service department, data analytics can help enterprises achieve a higher level of competency while keeping the investment to a minimum. When customers experience a seamless interaction with a business, they prefer to stay with the same business and even bring new customers to the business.
Human resources are very much a part of every organization. How can a business function without its employees? Advanced data analytics can help you identify the talent gap so that you can hire candidates with the exact capabilities and expertise to bridge the gap.
Regression analysis, classification analysis, association analysis, and cluster analysis are some data analytics team responsibilities to help the top management hire candidates who would become assets for the organization rather than a liability.
We have already talked about how data analytics can revamp and streamline business processes to increase the overall efficiency and productivity of the enterprise. SMEs have limited resources and budgets, which makes it even more important to optimize the use of resources without compromising on the quality of the output. While identifying short-term trends is easy enough for experts, accurately predicting the long-term challenges requires advanced technology. That’s where big data analytics deep learning, which is a subset of machine learning, and the all-encompassing artificial intelligence, help organizations become better at managing and optimizing their resources.
Advanced data analytics can help an organization achieve its goals by bringing a change in the business processes across all departments. It helps the employees and the management connect with each other on the same level. This is done by streamlining the communication channels within the enterprise.
Building an analytics organization requires effort, persistence, and the right kind of support from outside experts. For an organization to be truly successful, it needs to be powerful and popular in various relevant industries in the market.