Real Time Data Analytics

Did you know that real-time data analytics is one of the most critical aspects when it comes to big data? In fact, it is the most difficult part if you want to employ data analytics in your organization. It is true indeed that data analytics solutions help you gain some useful insights when it comes to your organizational data by managing the streaming of information sources, however, real-time data analytics comes with certain challenges. Storing loads of data and evaluating the same in real-time is a completely different game in big data analytics.

Again, if you want to process data in real-time, it often calls for error-forbearance, scalability, flexibility against data stream deficiencies, and most essentially, should be extendible. In this blog post, we will walk through the key challenges of real-time data analytics and their possible solutions. Read on to learn more.

The term real-time is ambiguous itself

As far as data consulting services are concerned, clients have a diverse understanding of the word real-time. When it comes to analytics, few people think that real-time implies receiving instant insights from loads of data, while others believe they are okay waiting for a long time between information collation and the response from the data analytics system. As various interpretations exist, it leads to conflicting needs. Let us explain this point with the help of an example. For instance, the executive-level managers did embrace real-time data analytics, however, but the higher management has a different perspective of the term and has dissimilar anticipations. Do you think that project will achieve success? Most likely, not.

You need to spend considerable effort and time collating elaborate requirements from your stakeholders. Your goal is to ensure that your entire team is on the same page when it comes to the implication of the term real-time. You need to figure what information is necessary for real-time, which sources of data to exploit. This is where data analytics solutions come into play.

Fine-tuning your internal business processes

When it comes to real-time data analytics, it entails a series of activities like collecting requirements, opting for the precise technology, designing the architecture of data analytics solutions, and addressing software and hardware challenges. Then, due to these technical jobs, organizations often do not pay heed to what they must do with their internal business processes.

Businesses use real-time data analytics for valid reasons, but not happy with your organization’s internal process, what are you supposed to do? For instance, imagine that you have a manufacturing business and are not satisfied when it comes to the repair time of your equipment. Machine or equipment failure is usually sudden and your maintenance professionals may take several hours to recognize the root cause of equipment failure. Then, they cannot mend the equipment, as they do not have the replacement part.

With real-time data analytics solutions, your need for the maintenance expert’s operation will vary. You would expect quick solutions and precautionary maintenance depending on the data collation process. To reap the maximum benefits out of the analytics solution, you need to revise your current equipment maintenance procedures, job descriptions, and key performance indicators (KPIs).

Your business can make the most out of real-time analytics when you do think of it as your final objective but perceive it as a useful tool as well as a starting point for enhancing your organization’s internal processes.

Irrelevance in real-time analytics architecture

When it comes to real-time data analytics architecture, it should have the potential to process information with speed. However, based on the data type and source, the speed may differ from milliseconds to even minutes. Again, the data analytics need to be developed accordingly.

The most essential aspect is that your real-time analytics architecture must manage the increase in data volume efficiently and requires scaling up when necessary. Additionally, the architecture must have the ability to capture data in real-time and offline data analytics. Then, running offline and real-time data analytics leads to conflicts and therefore, the designed analytics architecture should have the potential to manage the conflict as well as address the basic architectural problems or challenges.

What you need is a professionally designed architecture, which is a crucial success factor. That is why you need to know your basic architectural concerns. The typical real-time analytics architecture is a great foundation, but you can customize it more to ensure improved efficiency or performance.

Change in a business means several resistances

Using real-time analytics in a business adhering to conventional intelligence techniques may pose a great challenge. You might be wondering why. The key resistance comes from your current staff or workforce. Real-time data analytics may give your business new directions when it comes to achieving business goals and exploring new opportunities, a couple of times, it may seem like an interruption for your current workforce. Therefore, it leads to employee resistance within your organization when you implement new changes.

You need to avoid all employee resistances by clarifying why you are implementing real-time data analytics as well as the benefits it would shower on your business. You will need to persuade your existing staff of these changes and new ideas.

It is natural to face several technical challenges when implanting an enterprise-wide change. That is why you need to ensure the right training facilities are in place to develop employee confidence. Additionally, your company should ensure that all your employees have a proper understanding of the benefits concerning real-time data analytics systems and make themselves prepared to work with the same efficiently.

Even when you know all changes will benefit your organization, ensure your employees understand the same, share your knowledge, and on the same page when it comes to real-time data analytics. Your employee should know the benefits and background of the new system so that they work with new systems sans any hassles.

Issues related to data quality

One of the greatest challenges of real-time data analytics is data quality. Informed decisions need to be taken on real-time data and therefore, information must be entered precisely. When data isn’t properly entered, you may experience a domino effect, which means incorrect information spreading throughout your enterprise database and not simply a single spreadsheet.

Based on the findings of Gartner, 75 percent of companies experienced an adverse effect of incorrect data on their finances, and half of them bearing additional expenditures to settle the information. Data quality matters and imprecise information affects many facets of a business and not just one or two. Did you know that your entire organizational policy could be out of line if the data evaluation and understanding are incorrect? That is why you need proper analytics systems to ensure the best quality data.

To evade imprecise data, it is vital to take on the business-wide stand that all employees are data professionals. It is imperative to ensure precision, entirety, uniformity, and reliability amongst other things when understanding business data. To achieve the goal, everybody from customer service to stock managers, from the sales department to the bookkeeping division identifies with the operations of your business and depends on precise, correct data.

Wrapping up

When you use data analytics solutions, it becomes easier to deal with the challenges associated with real-time analytics. Analytics solutions will let your business attain quicker insights and manage streaming sources of data, thus giving your data evaluation more profundity.

Get a Free Demo