Customer Information Data Management Systems | Pitney Bowes
With Data Federation, customer knowledge is always at hand
As enterprises gain more data about their customers, they should take steps to make the most of it
Does your business have an ongoing and thorough view of its clients and their experiences? If you continue to rely solely on complex, time-intensive integration techniques such as batch Extract, Transform and Load (ETL) processes to feed customer data warehouses and inform business intelligence services, the answer is probably not.
Today, customer data is coming into most companies in fast and furious mode, emanating from a vast array of channels and touch points and being directed to a multitude of enterprise systems. The delays built into the process of physically moving and merging all that data for access and analysis take their toll on the currency of customer information, which businesses depend on to achieve insights to drive sales, marketing initiatives and other decisions. Delays may also impact an enterprise’s ability to inform quality, real-time customer service exchanges.
Onboarding fresh information for use in a data warehouse — say, unstructured social media that could help identify upcoming customer trends — and of manually adding report formats for new search queries is also a difficult and slow-paced process. It can further complicate a business’s ability to take timely action with a full understanding of customers’ expectations in mind.
Data federation tools, also known as data virtualisation tools, make it possible for businesses to have a centralised and highly current view of customer information across all channels, on demand. They give companies the opportunity to understand their clients and those clients’ experiences without requiring risky and time-consuming physical data movements that may introduce information quality and duplication issues.
With data federation, the overhead that comes with continuous data capture, transformation and movement is significantly reduced. The ability to leverage data from multiple sources and query it in the aggregate for business intelligence (BI) or other analytics is suddenly and dramatically simplified.
The case to federate
With the right tools, businesses can create an abstracted virtual data source that provides unified access to heterogeneous data sources where they live — on- or off-site — through a uniform user interface. Virtual copies of data from multiple sources may be queried as though they were a single, physical data set.
Then developers can bring additional data sources into the virtual view — including data from the cloud and NoSQL databases — with little effort. “In the age of the customer, where the proverbial bar has been set higher for making use of — and understanding — the data around one’s customers, it’s pivotal that new channels of information and data be accommodated into larger, more holistic views,” says Jeff Goldberg, global product marketing leader at Pitney Bowes. They must also consider issues such as privacy when it comes to sourcing feeds from social media sites and other external data for their use.
Accommodating this capability increases the agility of existing BI processes and the quality of information that can be delivered to users. “The whole key to flexibility is being able to try new things and roll out changes to a data environment with minimal impact on existing systems, and maybe in a way to achieve short-term business goals without making permanent changes,” says Scott Arnett, director of product management, Spectrum Data platform.
Dashboards, reports and front-end customer engagement systems all can have direct access to the virtualisation layer of data federation tools sitting atop systems of record without any impact on application performance. Client representatives, then, can have real-time data from multiple sources available to them upon request to add more accuracy and insight to customer dialogues. This data may even drive what otherwise would have been overlooked sales opportunities.
A win for access and analysis
The need for organisations to provide users increased access to a variety of data in a Big Data world is reflected in Achieving Greater Agility with BI, a best practices report from TDWI, an organisation that provides business and technical education and research about all things data. Half the respondents surveyed as of 2013 were either using or had plans to use data federation/data virtualisation technology.
In addition to expanding data access, businesses increasingly will look to further democratise data analytics. Taking analytics beyond the IT/developer set and giving more business analysts and other end users the ability to conduct customer data explorations and create queries specific to their own needs can increase an organisation’s ability to compete for customer attention, traction and value.
Some platforms already power the move to further end users’ ability to manage and manipulate federated data views. Armed with an intuitive, graphical and easy-to-use interface, users don’t need to be IT or data science experts to develop meaningful queries against existing data. What’s more, with data sets enriched with location intelligence, businesses can seamlessly tap into geocoding features and further enhance customer data. This capability can support analytics queries around demographics or proximity to business locations. In other words, businesses can create more holistic views of their data, enrich those views with additional data points and at last understand their clients’ full experiences, continuously and on-demand — and be poised to profit from that understanding in a simple, powerful and agile way.
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