- Automation
High-quality data is crucial for best-quality outcomes. However, the challenge posed by the complexity and sheer size of modern data makes quality data and outcomes challenging to achieve. Therefore, more and more companies are harnessing automation of processes to increase efficiency in data discovery, preparation and blending of disparate data. Automation frees up the analysts to focus on high-value activities, which are necessary for the growth of an organization. Furthermore, it contributes to the efficiency of processes. As the adoption of big data continues rising and making its way into the manufacturing, retail, financial and travel industries, a response to massive quantities of data and its importance in making critical decisions will become even higher.
- Democratization
Unlike the notion that only analytics and data science professionals with knowledge in big data can perform in big data, the truth is the opposite. All users can now analyze data on their own with the help of the right and easy-to-use analytics automation platform. Unlike in the past when IT experts were hired to do the analytics, analytics solutions and cloud computing power and open source tools have democratized the industry. Users of these platforms can drag and drop items and see the results instantly. Democratization reduces the cost and increases the upskilling.
- User experience
User experience is finally getting much-needed recognition from B2B organizations. The availability of easy-to-use smartphone applications that consumers enjoy has increased the expectations when in B2B. Users want simple and interesting engagements with analytics tools, and this is perhaps one of the good things that automation platforms give them. With the challenges that mundane manual tasks can cause, users can practice data storytelling by putting together data elements to show a bigger picture and insights that will be important to the business.
- Big data needs will spur innovations
As data sources continue rising, the increase will demand more storage. This means that the data storage methods will experience innovation to deal with this inevitable rise in data generation. Organizations will spend most of their resources storing data in different cloud-based and hybrid cloud systems optimized for big data. Furthermore, there will be an evolution of public and private cloud infrastructures as enterprises look for the economic and technical advantages of cloud computing. Apart from innovations in cloud storage and processing, organizations will seek new data architecture methods that will allow them to effectively handle the aspects of veracity, variety, and volume, which are the critical challenges in big data storage and retrieval.
- Analytics as a core function of a business
Many businesses are now embracing analytics as a mission-critical business function, unlike the past few years when it was used as a support function. To be successful, enterprises need support and commitment from the board and the C-suite. Therefore, they must invest in continuous analytics education and build a community led by a data culture, both inside an organization and with others.
