Monthly Archives: September 2017

Turning Big Data into Business Insights

The Reviews Editor at ZDNet UK, Charles McLellan writing recently in a special feature in the ZDNet magazine highlighted how the mere collection of data, which enterprises are doing increasingly efficiently, will not fetch many benefits to enterprises till necessary efforts to turn these into useful business insights are not carried out.

As Charles McLellan writes, the data collection is all pervasive and pervades enterprises of all sizes and definitions and the most important question today is about the how little control an individual as on his personal information, meanwhile astute enterprises with their Big data driven initiatives using large amounts of user data is targeting very specific groups with their services or in case of non-commercial enterprises seeking support to a particular goal or cause.

As he points out further in this article, enterprises to gain business insights from this deluge of information should have in place right planning, budget, tools, and expertise. This will enable them to analyze and get actionable data and help enterprises to implement revenue –generating innovations in a pertinent segment of the business to gain competitive advantage.

Charles McLellan quotes reports from IDC and EMC to project the total data generated globally by 2020 to around 44ZB (zettabytes), and another by Seagate’s Data Age 2025 to around 163ZB, and for enterprises with vast amounts of data coming in it will be easy to get overwhelmed with the possibilities. And with data coming in different types like structured, unstructured and real time, it should be understood that all data is not conducive to analysis. And according to IDC by 2025, 20% of the data will be critical (Data necessary for user daily life) and around 15 % will be hyper critical (Data with direct and immediate impact on user’s life).

With Artificial Intelligence and Machine Learning being the foremost technologies deployed to understand Big Data, the data available for use will be further restricted. In fact, according to IDC by 2025, only 15% will be tagged and so useful for AI /ML analysis and further in this only 3% will be suitable to be analyzed by cognitive systems.

The coming trends for the Big Data industry according to this ZDNet special report by Charles McLellan is  “ AI, Machine Learning, Automation and Cognitive systems”, along with “ data driven business applications “

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Behavioral Analytics is the next generation of business intelligence

Writing an opinion piece in the CIO magazine, Den Steiner – adviser CIO explains how behavioral analytics is turning out to be the new hope for business leaders in their quest for cost – effective mechanisms to analyze business data to gain competitive advantage.

Beginning how today enterprises collect extensive data in course of their business transactions, Den Steiner goes on to tell us this data can be utilized to gain a competitive advantage in the market place when it is streamlined and formatted in an efficient way to lend actionable information.

But with enterprises encountering the challenges of the need for powerful architecture, robust data – warehousing and separate data visualization, this process most times lead to perplexing workflow and foggy analysis resulting in a more or less a very low efficient system.

In their bid to overcome these lacunae, business leaders tend to concentrate their resources on a single channel say mobile activity or website visitors and their page clicks to focus on activity levels and conversion rates. This even though useful to a certain extent they would have made use of a very small set of data.

Enterprises with deep pockets tend to face challenges of data sitting in different silos requiring the services of a development team to get a factual understanding of different data sets as the present data integration process is complex.

In the above scenario, the advent of behavioral analytics offers a solution which unifies the entire of digital activity, making it easy for enterprises to trace the customer’s journey from start to finish.

As is the practice, few industries are faster off the starting block in adopting behavioral analytics than others, on –line merchants in their quest technology innovation are in the fore front but many other industries like cyber – security, gaming and fin –tech can benefit equally from investing in this business intelligence.

And Den Steiner makes a strong case for enterprises to make behavior and its analysis the center in their need to organize data. And is of the firm view that behavioral analytics is the next generation business intelligence.

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How AI helps financial institutions perform customer due diligence.

David McLaughlin, CEO, and founder of QuantaVerse – a provider of Data Science and AI for identifying financial crimes writing recently in the Upside – a new blog from TDWI dedicated to providing information on extracting actionable information from data tells us how AI is helping the financial institutions do due diligence on customers.

He starts off by informing us how financial institutions are forced to rely on expensive and cumbersome KYC (Know Your Customer) initiatives to conduct verification of account opening details and to get additional customer information and documentation. And even when efficiently run this processes fail to uncover the underlying motive of the account owners.

With Financial Crime Enforcement  Network (FinCEN) having come out with new Anti –Money Laundering provisions which in its new Fifth pillar requirement mandates account specific transactional review and analysis from May 11th, 2018, Until now the financial institutions had a client –centric approach to their customer relations and avoided review of individual accounts. But with the Fifth pillar provision coming into effect, Financial Institutions in order to improve their risk management proficiency  are now required to ascertain the accounting objective, account history along with volumes and value of transactions particular to each account.

And going further, financial institutions to mitigate risk also need to monitor, investigate suspicious client activity which involves constant maintenance and up gradation of client information.

These fifth pillar requirements may be addressed effectively by the adoption of new technologies like Artificial Intelligence (AI), machine learning and Big Data. Financial institutions can reduce money laundering risks by deploying solutions to analyze massive amounts of both structured and unstructured data to extract precise information to analyze and review a specific transaction.

Financial Institutions deploying appropriate Data science and AI technologies may address new requirements like Link analysis, Transactional analysis and it may also seek to undertake help from Outside Investigative Sources and Unsupervised Machine Learning Techniques to help meet the growing regulatory demands of state, federal and international regulators.

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