Big data analytics, the employment of advanced analytic techniques against enormous and diverse data sets, enables researchers, analysts, and business operators to more quickly make better decisions. Techniques include predictive analytics, text analytics, machine learning, data mining, stats, and natural language processing, and with the rapid advancement of big data analytics organizations can now tap previously unexploited data sources for significant insight. Says Jeremy Waite, Head of Digital Strategy at EMEA Salesforce Marketing Cloud, “In 2015 we learned that 90% of the world’s data had been created in the previous 12 months… The big data motto for 2016, therefore, needs to be ‘we must create more value from data than we capture’.”
(Infographic Source: Wipro)
Though the term ‘big data’ is relatively new, the concept is not, and even in the 1950s basic analytics were used to discover relevant trends and insights. As the computing environment has advanced, as well as access to new forms of data through sensors, monitors, IoT applications, and wearables, data analytics has taken on a more prominent role. A few years ago, analytics would have provided information for future decisions, but today businesses can use big data analytics to support immediate decision-making processes. Big data analytics in its most recent form allows organizations to work quickly and with agility, promising improved products and services along with reduced costs.
The benefits of big data are much lauded, but actually bringing these benefits into a business requires detailed planning and infrastructure provision. Cloud computing has reduced the entry requirements for effective use of big data analysis, but it’s still necessary to invest in features such as data collection and storage, and data analysis and output.
Collection & Storage
The array of collection devices is almost endless with innovators daily finding new means of gathering records, facts, numbers and statistics. Social media and customer feedback serve the essential role of personalizing data, while beacons, wearables, and IoT sensors capture endless bytes of seemingly meaningless data. In its raw form, this data can be overwhelming and incomprehensible, and often post-collection is simply stored safely away. Businesses with effective big data analysis tactics in place are careful not only to install necessary collection devices but implement storage systems that are accessible and supportive of the following phase.
Data Analysis & Output
Processing and analyzing data extracts insights through programming languages and platforms, including software from vendors such as Google, Oracle, Amazon Web Services, Microsoft, and IBM. Add to this, a host of startups have entered the market, often with simpler but more focused solutions. No matter the chosen tool, the data analysis process first requires that the data is cleaned and formatted. Following this, the analytic model is built, and finally, inferences are made. Once this analysis is complete, it’s important that the results are properly communicated to the relevant business departments and given the value they merit. Better a summary of recommendations with key substantiating figures for prompt action than a heap of restructured data easily ignored. Useful data output is as essential as each step which precedes it, and the effort put into making it attractive and easily understood shouldn’t be underestimated.
In 2014, big data analytics in the cloud was making waves; today it’s the norm. This year, analysts are predicting high growth rates, and the IDC expects the worldwide big data technology and services market to grow to $48.6 billion in 2019. The International Institute for Analytics believes that automated data curation and management will play a bigger role, with an ease in the analytics talent crunch, and analytical micro services facilitating embedded analytics. The IDC, however, believes this specific skills shortage will persist due to increased demand for such expertise, and Forrester echoes their expectation, suggesting the growth in degree programs launched globally will not be enough to meet the ‘huge demand.’ Today, big data analysis is promising more value to organizations, but the next few years will require a higher level of input and collaboration as businesses attempt to constructively engage with it.
By Jennifer Klostermann
