Most companies have inadvertently locked away the value of their enterprise data, because they can rarely harness the right information at the right time. When they finally do access information, it is often inaccurate, incomplete or unavailable. This is costing companies dearly in the form of lost revenue opportunities, greater operational costs, and increased risk and non-compliance.
According to a leading analyst firm, 90% of internal data requests are for data that is less than a year old – i.e. the business focus is on fresh data access, while traditional IT focus and investment has been on storing and replicating (not-so-fresh) data. Also this new data, which is growing in volume and complexity, is increasingly challenging to access as it resides in various silos within and outside the enterprise.
In order to derive value from all this data, companies need to unify these independent silos and share the data in the required time and format with any number of users and applications. However, the traditional tools used to integrate and publish data fall behind in areas of flexibility and timeliness, so companies are now looking at new alternatives like data virtualisation.
This technology provides a unified view of the disparate data sources and enables the real-time transformation and delivery of data in different formats (reports, web services, mobile applications, etc.). As a result, data virtualisation enables companies to become more agile and deliver the right data at the right time to the right consumers resulting in significant impact from a revenue, efficiency and risk/compliance standpoint.
In a business landscape complicated by changing customer preferences, disruptive innovations, and new business combinations, information agility is a crucial component of any revenue and growth strategy. Information agility allows companies to make informed and quick decisions that can impact revenue drivers like time-to-market on new products and services, competitive action including pricing changes, customer service and retention, and acquisitions to increase market share.
For example, a buyer in a leading fashion retailing firm using data virtualisation is able to access not only sales and customer data from internal ERP and CRM systems, but also customer feedback from social media sources in order to get a consolidated view of last year’s fall line before making this year’s decisions. And this data is available on-demand, not in days or weeks.
Data virtualisation provides this through built-in connectivity to a range of structured and unstructured sources and real-time delivery to users and applications. Reusing this data-as-a-service in combination with store inventory data allows a retail store clerk to up-sell and cross-sell their customers with a better understanding of their preferences.
Data virtualisation’s capability to merge enterprise data with semi-structured information from websites in near-real time helps this company to track their competitors’ promotions on high-margin product segments and quickly respond with pricing and discount strategies to increase market share.
Kaizen (the Japanese philosophy of continuous improvement) experts say that 80% of the work that goes on in any organization adds no value to their customers. This applies to various business processes, starting with data acquisition tasks like searching for data, requesting access, waiting for IT to respond, re-working the data, etc. Data Virtualisation presents an easier alternative exposing pre-integrated business entity views of data by masking source complexities and delivering timely access, which improves productivity.
A multi-brand telecommunications retailer is using data virtualisation to synchronise customer, product and order data between its retail systems and its partners’ web based activation systems. This could be done because the platform could plug into the multiple disparate systems, track changes and make updates accordingly. Thus, store employees had access to accurate and current data at all times, reduce data entry time and increase point of sale productivity by 50%, reduce client in-store waiting times by 75% and still provide better customer service.
Information agility not only helps humans, but machines as well. A fruit processing and packaging company in California has increased efficiencies by deploying data virtualisation to provide real-time operational intelligence and synchronisation between fruit cleansing, pitting and canning assembly lines. So, if a line malfunctioned, the reduced processing rate would be detected and synchronised while also sending an alert to the supervisor and maintenance staff so they could respond and avoid hours of downtime and wastage.
Risk and compliance have been matters of great concern for organisations due to dynamic and competitive markets and growing regulatory scrutiny. On the risk front, the lack of a complete view of all data exposes companies to higher business risk and limits their ability to respond to disruptive market events. Banks and insurance companies are facing tighter controls and have to very accurately assess risks for casualty events, credit defaults or financial market shifts.
The information to do this is available, but they face in accessing data spread across legacy systems, excel files, emails and third party sources and in integrating the most recent data into their systems. Incomplete data can lead to the incorrect valuation of the risk leading to potential losses. Compliance also comes with its own share of expensive data demands.
For example, the Dodd-Frank Act requires financial services companies to disclose risky derivatives like credit default swaps, provide data on hedge fund trades, and disclose methodologies for credit rating agencies, all of which require increased information access.
The same act also has provisions for extractive industries to disclose payments made to Governments in exchange for access to extractive resources and requires that information be broken down not only by country and project, but also by categories of payments (royalties, taxes etc…). As regulations often change, data virtualisation provides the flexibility needed to quickly access and combine, a broad range of source systems and reuse data entities for agile reporting for risk and compliance to keep pace with regulators’ demands and helps avoid fines and minimise roadblocks.
It’s no surprise that data is a gold mine and what creates value for the business is the ability to tap into this valuable asset in a broad, flexible and timely manner. The focus of CIOs everywhere is shifting to information agility in terms of disparate data access and (re) use in a business-friendly format, rather than the traditional pre-occupation with data acquisition, storage, and replication, while continuing to ensure and manage appropriate levels of data quality, governance, security and performance.
Because of its ability to deliver on these capabilities on an enterprise grade level, data virtualisation has become a must-have technology which has been successfully demonstrated in large deployments at leading companies across several industries.