Future of Data Analytics

According to a McKinsey report, data analytics has the fastest growth rate of 16-18% over the next five years. This means data analytics will gain momentum in the foreseeable future and be the core of new technical solutions. Data analytics has evolved from descriptive analytics to predictive analytics. With AI, cloud computing, machine learning, and IoT, analytics is taking a new form capable of completing complex operations. As technology evolves, we continue looking for more advanced forms of analytics.  

 

Today, business planning strategies rely on business intelligence (BI) and analytics. But will these strategies be outweighed in the future? Will business analytics completely change in the next time? What are the chances that the business will stay competitive? In this blog post, we see how analytics’ future looks and what’s in the bag for us. 

 

Table of Contents

 

  • Changing landscape
  • Analytics of the future
  • Conclusion

 

Before we move ahead, you can check out the best big data certification for data analytics offered by Global Tech Council. 

 

Changing landscape

 

Data analytics can change the way we live and do business in the future. Analytics is already present in our technology devices and involved in many life decisions. From avoiding traffic jams while navigating to identify waste in business processes, data analytics is growing. At present, all the organizations cannot exploit analytics’ benefits due to a lack of resources and poor data quality. However, a KPMG survey suggests that in three decades, this issue would become obsolete. Data analytics is believed to make the impossible possible in this data-driven era. Today, every company is investing in data analytics to keep up with the competition and other unknown developments. Here are some developments you need to know:

 

 

  • Interconnectivity 

 

 

Interconnectivity is the key to building a cohesive data analytics machine for a business because of increased reliance on BI and new internal tools coupled with networks and IoT devices. To pan well and stay firm in the coming years, it is necessary to plan for securing talent well in advance. There is also a need to create process strategies for maintaining clean data across systems. 

 

 

  • Management of Data

 

 

Managing source data and ensuring accuracy in a consistent format is paramount for data analytics and BI. More valid is the data going in, more useful will be the data that is coming out. Companies heavily rely on information for running the business. Thus there is a need to find solutions to this problem. The continuous increase in data sources adds to the challenge. The threat of security issues is ever-growing, and the public is more privacy cautious than ever. These challenges can be easily recognized, and significant investment is required to address the challenge. 

 

 

  • Machine Learning

 

 

Artificial Intelligence and Machine Learning have created endless options. In companies’ race to harness the power of technologies for customer service, and providing value to new service in unique ways, big data training with knowledge of machine learning is essential. 

 

 

  • Large Data Networks

 

 

Vast data stores or advanced data networks are increasing, adding value to companies. The extensive customer data in these networks can supplement the already existing data. They will complete gaps in personalized customer service. This also creates potential for new services to address unmet desires.

 

 

  • Wider user adoption

 

 

Analytics focuses on increasing natural language and usability to enable business users to extract data and report without understanding the underlying algorithms. This would increase efficiency and create a newer need for adoption throughout. The broader adoption by business users would alleviate problems created by the shortage of people with data analytics certification. 

 

 

  • Shortage of data specialists

 

 

There is an apparent shortage of big data experts and data scientists today. This issue can worsen in the short term. To increase a company’s competitiveness in the market, it is essential to plan today. A company can address the issue with unique incentives or by big data training for its data analysts. 

 

Analytics of the Future

 

Automation plays a huge role in the future of data analytics, making it invaluable to companies that want to streamline the process of analysis. Here are some forms in which we would see analytics in the future:

 

 

  • Relationship analytics

 

 

Data analytics is used to find relationships between a pair of variables. The main issue today with the current analytics solution is isolation. Due to this, organizations are missing out on the big picture. The future would see organizations connect multiple data sources using multiple techniques for a comprehensive analysis. This is possible with relationship analytics for entirely unrelated data. This would allow organizations to maximize the value of their data. Relationship analytics is the future because of the extra dimension it gives to analytics procedures. 

 

 

  • Augmented Analytics

 

 

Augmented analytics is formed by the integration of data analytics and business intelligence. It can scrub raw data for valuable parts of analysis and automate certain parts making the data preparation part easier. Data preparation is elaborate and takes up 80% of the time. The number of data sources has grown with the proliferation of IoT devices and social media engagement. The machine learning and NLP component of data analytics allow platforms to understand data more organically. Augmented analytics streamline the entire process of generating invaluable insights. As this analytics is automating menial work, it would become the future of analytics. It also opens up analysts to advanced technologies like smart data discovery. 

 

 

  • Continuous Analytics

 

 

There is a need to generate faster insights to take full advantage of data analytics in the future. This is where Continuous analytics comes into the picture as it allows organizations to analyze streaming data continuously. This field is exciting as it performs proactive alerts to end-users and real-time updates. This would be possible due to the integration of DevOps and Big Data. It is a vital part of the future of analytics as it can improve production efficiency. With continuous analytics, an organization can fully exploit data from IoT. 

 

Conclusion

 

Data analytics is growing because of the proliferation of new technologies, such as AI. The future of data analytics would allow access to insights in seconds and at lower costs. If you want to be a part of this future revolution, sign up for big data analytics certification today!

 

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