Big data has changed how software is developed and the processes practiced by a Software development company. The phenomenon has streamlined the process of development with readily available data these days.
Software companies develop solutions that give any business organization an edge over the competition. With the explosion of Big Data, developing software solutions have been made simpler and more effective. Last year was a landmark for big data, with more organizations that store, process and extract value from all forms and sizes of data. This year, systems which support big volumes of structured and unstructured data would continue to grow. The market demands platforms that help data custodians secure and govern data analytics while empower the end users to analyze data. The systems would mature to operate well inside the enterprise IT standards and systems.
THE PHENOMENON THAT IS BIG DATA
Big data analytics could be described as high-velocity, high-volume and/or high-variety information that need new kinds of processing to enable improved decision making, process optimization and insight discovery. Today, there are just so many machines and devices that enable people to seek and obtain information in a way that people have grown accustomed to. It resulted to a mountain of data bytes. The data analytics phenomenon has given rise to several technology trends, such as how software development firms build solutions and applications.
TOP TEN BIG DATA TRENDS IN 2017
1. Proliferation of Big Data. Data analytics proliferation has made it critical to analyze data fast to gain valuable insight. Organizations should turn big data terabytes that’s not used, classified as dark data into useable data. Data analytics has not yielded yet substantial results that organizations need to develop new insights for innovative, new offerings to gain a competitive edge.
2. Using Big data to boost CX. Using data analytics to boost CX, through moving from legacy to vendor systems, during M&A, and with upgrades on the core system. Data analysis with self-service flexibility to harness insights fast about leading trends, together with competitive insight to new customer acquisition growth opportunities. Using data analytics to understand customers better to improve top line revenue via cross-sell/upsell or remove the risk of revenue loss by minimizing churn.
3. Hello to Predictive Analytics. Predict future behaviors and events precisely to boost profitability. Take a leap in boosting fraud detection fast to minimize risk exposure of revenue and boost operational excellence.
4. Wide Hadoop adoption. More and more companies would be adopting Hadoop and other big data stores. In turn, vendors would introduce new, innovative Hadoop solutions fast. With Hadoop in place, companies would be able to crunch big amounts of data with advanced analytics to search for nuggets of valuable information to make profitable decisions.
5. Move towards informatics and the ability of identifying data value. Informatics will be used to help integrate collection, analysis and visualization of complex data to get revenue as well as efficiency value from the data. Furthermore, it also taps underused resource data to boost business performance.
6. More focus on data analytics that are cloud-based. Moving big data analytics to the cloud hastens the adoption of the current capabilities to turn data into action. Lower costs in ongoing operations and maintenance by moving data analytics to the cloud.
7. Convergence of the Cloud, IoT, Big Data and CyberSecurity. Data management technologies convergence, like data quality, data analytics, data preparation, data integration and more. As people get more and more reliant on smart devices, machine learning and inter-connectivity would be even more important to protect the assets from cyber security threats.
8. Achieving maximum BI with Data virtualization. Data virtualization unlocks what’s hidden within big sets of data. Virtualization of graphic data enables companies to retrieve and manipulate data on the fly, regardless of how data is formatted or where it’s located.
9. Boost digital channel optimization and Omni-channel experience. Deliver the balance of traditional channels with digital channels in order to connect with a customer in the channel they prefer. Looking for innovative ways continuously to boost CX across channels to achieve competitive edge.
10. Self-service data preparation and analytics to boost efficiency. Tools for self-service data preparation boost the time to value, allowing organizations to prepare data despite of the data type, whether structured, semi or unstructured. Lesser reliance on development teams to massage data by introducing more self-service capabilities to provide power to a user and in turn, improve operational efficiency.
Talking about the big data future is somewhat beside the point, since it’s very much a ‘here and now’ phenomenon. A lot of market leaders are using big data and data analytics already in way that may appear futuristic to their lagging competition. Early big data adopters and top performers already are driving forward on all the big data fronts. They have made urgency of shaping the future. The future of big data analytics is now and it will arrive faster for some companies than others.