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Big data analytics has moved beyond historical analysis and now predicts future consumer trends. Find out how it began and how it continues to shape business.
Remember that cult-classic Ashton Kutcher film that no one could ever understand? To refresh your mind, the basic premise of the Butterfly Effect is that a butterfly that flaps its wings in China can, through a series of proceeding events, change something on the other side of the world. Big data analytics make it possible to take the flap of the wings and predict the future outcome. The amount of customer data retrieved from customers' social media, purchasing history, and Google searches is staggering. In five years, Forbes predicts that there’ll be 50 billion smart devices all developed to collect, analyze, and share data. The predictive possibilities are endless.
The gathering of data has pervaded society for thousands of years, from the inception of agriculture to the mapping of the stars. Big data, on the other hand, only began gaining popularity in the 2000s. There are 4 "V"s that distinguish big data from regular data analytics:
Unlike Google, Facebook, Microsoft, or Amazon, small and medium businesses (SMBs) were severely restricted in the use of predictive analytics due to the fact that the big data market had massive barriers of entry. That is, until recently. Now cloud-based web models have significantly reduced these barriers with releases such as Amazon’s Hadoop and Spark.
The next biggest challenge for SMBs was data integration. In the past, SMBs simply couldn’t find the time to integrate all of their data. However, with the release of new standards such as JDBC, ODBC, ADO.NET and OData, timely data integration became feasible. Data Connectivity solutions like Progress DataDirect enable customers to integrate any application with any data source whether on-premise, cloud, or hybrid (across firewalls).
For a while now, businesses have used data analytics to create a competitive advantage through operational efficiency. Retailers, for example, could install several BI applications to analyze inventory, track social media, discover buying patterns or analyze sales, but the problem with using multiple products is that it often ends in a sea of reports with no plan of action.
To find out how to solve this problem, we need to take a deeper look into traditional data analysis. It is generally classified into three different categories: descriptive, predictive, and prescriptive analytics. These forms of data analysis help businesses predict the future and track patterns to maximize profit. In the past this was limited by poor processing power or lack of data, but with technologies like Hadoop and NoSQL emerging, the mining and processing of huge amounts of data has become widely accessible.
Now that retailer can not only generate BI reports based on transactional customer data, they can run complex analytical models and aggregate data based in clustering, regression, segmentation, neural networks, etc. at a much higher level. If you were only previously able to price goods based on traffic data and time of year, you couldn't predict how climate change will affect your suppliers, how stock market fluctuations affect your customers, or even integrate the weather and analyze the affect of storms on customers. Big data platforms allow you to free cash flow, plan inventory, and create predictive marketing plans based on your data.
At one point, ideas like this were considered sci-fi, but they are now a part of everyday life for many businesses.
There are many things to consider when evaluating big data solutions, and picking a vendor is an important step. In the early stages of Business Intelligence, the only people qualified enough to analyze the data were data scientists or coders. As it has advanced, managers, business analysts, sales, Ops and more can increasingly use these tools intuitively through easy-to-use graphical visualization interfaces. When you are searching for a vendor, definitely make sure to identify the level of expertise of the stakeholder who will use the tool.
Next, we must identify the needs of the business. A large enterprise with lots of opportunity for investment may be looking for a larger, robust, comprehensive solution. The giants in this space are Oracle, IBM, and SAS with their deep, broad solutions like IBM SPSS Statistics, Microsoft Revolution analytics and SAS Enterprise Miner.
Alternatively, a company may be looking to find a solution that covers most use cases at a basic level and specializes in a specific set of algorithms. Some standalone big data analytical platforms like Microstrategy, 1010data, and RapidMiner offer innovative approaches to big data problems.
Even after sorting through some of this basic advice, you are probably left with a few high quality choices in mind. Don’t fret—the next steps would be to identify data requirements, integration constraints, and collaboration and scaling needs. Developing an RFP helps answer these questions and makes your decision much simpler. Big enterprises are the veterans and come with a sense of reliability and experience. New solutions, although sometimes unstable, are much more accommodating and quickly adapt their solution to fix your needs.
Nishanth Kadiyala is a Technical Marketing Manager at Progress. He has worked as a Software IT professional for 3 years during which he actively pursued several technologies including database designing, SQL querying and Cloud Computing. He is currently pursuing his MBA at UNC Chapel Hill and concentrating in Marketing. At UNC, he became proficient with data analytic tools such as MEXL and R. He is interested in the SaaS, Cloud and data integration technologies that are revolutionizing the world we live in.
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