As more technologies are introduced for businesses to leverage for growth and improved performance, the importance of using data wisely comes into focus. Modern businesses generate tremendous amounts of data that hide many insights that could be of great benefit to the business’ progress. Therefore, businesses today have to figure out not only how to protect and secure this data but also to use them for benefits.

Before big data gained a foothold, businesses simply focused on customer experience & satisfaction as the best approach to grow faster. Now they realized the secret to accelerated growth lies in the data they generate. There has also been a shift in focus from customer-focused processes to operational performance improvements. The data from operational processes can be analyzed to help businesses tweak their approach to faster growth from the inside out, instead of investing lots of resources in how they appear fine to their customers.

The principle is rather simple really.

If you are good inside, you will be great outside.

In this blog, we will explore why operational analytics would be all the buzz in the coming years and how operational analytics can benefit modern businesses.

The Many Merits of Operational Analytics

More profit

Identifying and reducing unnecessary expenses is a good approach to increasing the profit margin. This is one of the primary goals of any business today. They can do this better if they look into their data. Operational data analytics helps businesses identify areas that need improvements and processes that need to be streamlined. This contributes to reducing expenses while improving efficiency, subsequently resulting in higher profits.

In 2017, Capgemini Consulting published a Whitepaper with their research on operational improvements and their benefits to organizations. The research had found that over 70% of the surveyed companies were more focused on operations than consumer experience. The research also found that operational improvements can lead to a raise in profits to exceed $115 billion globally every year. Customer analytics can drive only about $38 billion yearly in profits according to the report.

Better decision-making

Many companies often rely on reputed consultancy firms to make big decisions. If the business is willing to pool the resources they invest in consultancy companies to operational analytics, they can make decisions themselves; a more cost-effective approach indeed. Proper data analytics helps businesses make decisions quickly and even proactively.

Competitive edge

The advancements in Artificial Intelligence and Machine Learning opened up new possibilities for analytics and improved data analytics accuracy significantly. Big data analytics is known to grant competitive advantages for businesses provided it’s fed operational data instead of customer data. The results would help them save money and make better, more profitable investment decisions.

More satisfied customers

This may sound counterintuitive when this blog is all about shifting focus from consumer-centric processes to operational analytics. Investing in operational analytics does come with benefits for the business’ consumers as well. Without operational analytics, it may take a while for businesses to identify customer experience pain points or the factors that are causing the drop in customer satisfaction.

Data can speak faster with operational analytics enabling businesses to identify performance issues and fix them right away leading to improved customer satisfaction and more satisfied customers.

Holistic view of data for easier data management

Operational analytics grants a business’ stakeholders a holistic view of the business data facilitating more efficient data management in the process. It’d be easier to identify system issues in real-time and to devise an appropriate data security strategy as well. One other great advantage is that the stakeholders will be able to discover the interrelationships between certain networks which means root-cause analyses (when necessary) would be more efficient and faster.

More employee engagement

With all the data stored in a central repository, it can be shared with the employees as well to gain insights from them. This way the employees will also be empowered to contribute more to the company’s growth and success. The approach essentially encourages collaboration wherein all the teams in the entire organization will work synchronously for the company’s success.

Conclusion

Operational analytics is still seen as a new concept by many organizations that are still focused on customer-centric improvements. There evidently is a shift in trend but organizations need to keep in mind that operational analytics isn’t something that delivers great results overnight. It takes time as there are many factors involved including the issues associated with data silos, accessibility issues when it comes to third-party data, and the lack of mandate from stakeholders. Nevertheless, this is the right time to see what your company’s operational data is hiding and how you can leverage those insights to soar higher with changing times.

AOT can help you analyze your operational data with robust, bespoke analytics solutions with cognitive capabilities. Let us know if you are ready to give operational analytics a go this year.

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Over the years, businesses utilized a number of advancements – both technological and industrial, to accelerate their growth and increase their profits. Of all that which businesses have leveraged so far, one particular advancement stands out for its massive potential – Data Analytics.

With extensive data analytics, businesses can uncover valuable insights from the tremendous amounts of seemingly senseless and random data they generate. Armed with this information, organizations can tweak their business strategies for better results.

What makes this ‘big data’ awesome is the fact that it shows great promise for all types of enterprises irrespective of their sizes. Through big data analytics, enterprises can not only increase the revenue generated but also identify security vulnerabilities and gaps in their services and products. Big data is still an evolving technology however, and like every evolving technology, it is also shrouded by a lot of myths and misconceptions.

This blog debunks a few of the most popular myths that deter businesses from checking out big data.

Myth 1: All that matters is size

The first thing that anyone can think of when hearing the term ‘big data’ is the size. As such, there seems to be a myth that the main defining trait of big data is the sheer size or volume of data involved. This is not the case. Though size is certainly one of the main characteristics of big data, it isn’t the only important one.

The volume of data involved demands sufficient storage systems. But big data also requires high velocity data processing mechanisms to deliver relevant, more up-to-date outcomes. The data come from various sources which means the outcome may vary from what was expected. The data being handled by big data should be also be veracious and the insights from these data should provide value.

From all these facts, we can infer the most important traits of big data other than size or volume.

Velocity. Variety. Veracity. Value.

Neglecting these features can result in an improper implementation of big data that can complicate simple solutions and deliver irrelevant results while wasting resources.

Myth 2: Big data is a collection of high quality data waiting to be processed

Big data is a collection of a lot of data sets that can include both relevant and irrelevant data. All that data don’t necessarily have to be of high quality. As a matter of fact, without proper data filtration mechanisms, big data analysis can result in a lot of data quality errors.

Many organizations opt not to clean the data before processing to avoid the hassle of dealing with such large amounts of data. Data cleaning also requires the organization to invest more in big data. This practice most likely ends up with big data failing to do what it’s expected to do. For big data analysis to be effective, the data should be cleaned. This helps the system provide more accurate results.

Myth 3: Big data predictions about a business’ future are highly accurate

This myth is based on the assumption that the volume of big data is directly proportional to the accuracy of big data analysis results. The more data one feeds into the system, the more accurate the results will be. But that’s a myth however. Data certainly hold great value for modern businesses but there are other factors that are just as important if not more.

A big data system will never be able to accurately predict a business’ future by simply analyzing data. Factors like economy, technological advancements, human resources etc. directly influence the success of a business, and data simply won’t be factoring in everything that directly or indirectly influence the business’ success. What big data can do is extrapolate future possibilities by comparing and analyzing historical data.

If the business uses real-time data instead, the system will be delivering predictions based on a probability theory i.e. the results still won’t be certain. However, it’s true that the accuracy of forecasted results will be higher if the data fed are relevant and of good quality.

Myth 4: Big data is expensive

That’s the myth all right. This is the fact – big data ‘was’ expensive. Back when big data started turning heads for its potential, only big organizations and government bodies stepped forward to invest in it. The investments weren’t small either as organizations required large-scale data centers and talented big data professionals to wield big data properly.

But time has changed. As big data garnered praise from all directions, many vendors stepped forward reducing the licensing costs of big data tools and making them much more affordable to small medium-sized businesses. In addition, new tools and techniques are also being introduced to help smaller businesses perform big data analyses.

Myth 5: Big data is centered on machine learning technology

Machine learning certainly is associated with big data. But big data isn’t centered on machine learning. New generation big data analyses make use of complex machine learning algorithms to derive sensible and relevant insights from massive data sets. That’s the whole concept of Machine Learning – using data to uncover information that can help an organization or an individual make meaningful decisions. Both technologies are used in conjunction to deliver expected results. Big data can exist without machine learning.

Myth 6: Big data is all about analytics

What’s strange about big data is that it has multiple definitions, and most of them are accurate. A simple Google search would get you at least a dozen different yet valid definitions of big data from various sources. While some consider it as massive data sets, others see it as an analysis technology that can process massive data sets. The fact is that big data is something much bigger than data analysis with many capabilities to solve a variety of problems with data.

Conclusion

For big data to truly live up to its name in an enterprise, it should be seen as something more than a lot of data being processed. It needs to be managed and overseen by experts who can tell myths from facts.

If your enterprise is planning to leverage big data and is looking for expert guidance or automated solutions, you are at the right place. AOT’s deep expertise in a wide array of technologies including big data and AI/ML can ensure maximum big data ROI. Drop us a message to get started.

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