Big data is nothing but the confidential information storage center, which needs the support of AI to manage the huge volumes and function seamlessly.
In most organizations, big data storage is spread across different computers, on-prem, or on the cloud. The analysis of such an incredible and useful source of data, is best done with AI based tools and applications.
The insights derived by AI tools from both structured and unstructured data, effectively have the power to change the course of a company’s business growth. The World Economic Forum in 2016 has estimated an increase of stupendous US$100 trillion in social value and global business by 2030. Most of this growth will be driven by the power of data collated today!
A report suggests that, on average, every day, humans create about 2.5 quintillion bytes of data. If all these data are well-utilized, they will surely allow humans to get an improved view of the future. In business, this could be the difference between survival, or liquidation- or explosive growth. To be precise, data is valued to assets, which allows companies to get a hint of the future through advanced analysis.
AI technologies will probably be one of the biggets drivers for this growth, if studies from PwC and McKinsey are anything to go by. They estimate the increase in AI tools business to touch US$15.7 trillion, and be around US$13 trillion of the annual GDP by 2030. However, there are a few significant challenges that companies face concerning big data, which could be easily resolved by adopting technological changes:
Diversity in IT source system
Storing data is always a complicated process, and securing, maintaining/managing it is even more difficult. The average Fortune 500 Company has thousands of enterprise IT systems across diverse formats, with mismatched references across data sources and duplication errors. Such diversity only complicates the situation and creates chaos.
High-frequency data management
Data flow is real-time, so critical issues like censoring of data still stay unspoken. So, high-frequency data management not only complicates the process but multiplies the vulnerabilities and risks. AI can help sort and censor the data as and when they flow in.
Organizing data content from diverse sources
Since big data is gathered from varying and wide varieties of sources, their formats are different, and most of it is unstructured. So, even to structure the data into analyze-able bites is challenging and involves a lot of tools. This is merely to differentiate them and put them across diverse channels before conducting the in-depth analysis. One more added issue is data clarity, as some files don’t even comply with the set minimum clarity bar.
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Looking at all these issues, AI can be the superhero sorting them all. Enterprise analytics and IT team need to provide advanced tools to empower employees with diverse levels of data science proficiency to function effectively with large data sets, and perform smart predictive analytics using a unified vision.
Resolving the big data issues
Data Scientists could potentially be the magicians that will derive insights form the humongous amounts of Big Data in the market today. The predictions and insights could be the deciding factor for nay industry. Many CIOs feel ML algorithms are of great use as it facilitates the necessity to receive new data, generate outcomes, and have some decisions or actions to be made based on the outputs. Multiple algorithm libraries are widely available to data scientists today, and they can use these to create the highest quality actionable insights.
Big data issues like unstructured data, bad clarity content, data lakes, etc. are problems that could be demystified with the help of ML, data science applications, and with efficient application of AI tools.