Data analytics has become a chief driver of competitive advantage in the last few years. Given its critical importance, it’s essential that businesses stay current with top trends in the data analytics space, along with how those trends influence a number of related technologies.
Those related technologies include cloud computing, the growth in artificial intelligence software, machine learning, and edge computing, among several others. Let’s explore some top trends shaping the future of data analytics.
6 Data Analytics Trends Driving Progress
Growth of AIOps
The growth of AIOps is parallel that of data analytics. This is because it addresses the biggest issue that data analysis faces moving forward: abundance.
With the evolution of 5G and the Internet of Things (IoT), as well as higher consumer and user analytics, IT teams handle far too much data for traditional methods to handle. AIOps accounts for this, and moves towards a holistic approach towards analyzing data. Expect AIOps to accelerate the transition away from data silos.
The rise of AIOps will bring a number of benefits for how businesses leverage data and analytics. One major benefit of the growth of AIOps is preventing alert fatigue. Because AIOps account for a massive amount of data, software solutions built with AIOps at their core can filter through alerts by understanding which events are essential and immediate, and which are less important outliers.
Data democratization refers to the practice of making data accessible throughout all departments of a business, not just the C-suite and IT team. Data democratization is at the core of digital transformation.
This idea of understanding and accessing data no matter your tech expertise can bring about new self-service data analytics tools. We’re already seeing this with low-code solutions.
There are a number of benefits to data democratization to look forward to:
- Customer intent. Because data democratization allows for multiple departments to analyze and leverage data sets, a company’s understanding of customer intent will grow to be more diverse.
- Efficiency. Data democratization will usher in wide use of efficient tools such as AI analytics and cybersecurity software. On a deeper level, employees will truly understand the data they have access to, which minimizes confusion through cross-department communications.
- Customer service. Because data democratization reveals a more diverse scope of intent as well as greater efficiency, the customer experience is naturally improved for many businesses. One example of this is your help desk team having access to preferred contact information and previous shopping data. This helps tailor your customer service.
As mentioned above, low-code solutions are on the rise. A huge reason for this is the growth of data democratization and the industry-wide embrace of data analytics as a whole. This rise of solutions that allow for data analysis in a user-friendly manner can bring about major shifts for businesses.
One of the major benefits behind the rise of low-code solutions is improved access to analysis of customer journeys. Low-code solutions allow for departments other than your development and IT teams to analyze data. Giving other departments this access will not only grow company culture and provide more in-depth analysis, but also upskill employees as a whole.
With the rise of data democratization on the horizon, expect the overall experience of attaining data to grow easier as well.
One of the main ways this is expected to occur is through stronger data integration in software solutions that employees already use. Instead of data analytics being siloed away into their own programs, insights can directly show up in applications and interfaces businesses already work with.
Data integration goes hand in hand with greater efficiency as well. This is because users can receive relevant data and information directly without switching software. This eliminates potential user error due to the lack of searching that employees need to do to find data.
Small and Wide Data
The idea of small and wide data has grown as a counterpart to big data. This trend points to a new way of thinking about and analyzing data sets.
Wide data enables analysis from a variety of unstructured and structured sources. This wide variety allows for greater contextual understanding, which could lead to better or more unique solutions. Small data extracts from data models that have less data, but still offer unique insights.
Combined, small and wide data allow for fresh, insightful contextual lenses for data analysis.
Data Analytics at the Forefront
Finally, expect to see data analytics move increasingly to the forefront of business operations. Businesses historically saw data analysis as a secondary function. This shift to data analysis as the leading aspect of business operations is driven by the competitive edge offered by analytics, and by today’s technical infrastructure, which supports faster processing.
One of the main benefits of this embrace of analytics is the improved agility and responsiveness of enterprises. Scalability and improved revenue will surely be implications of data analytics entering the forefront of business operations.