While seeking authorisation for the test, I had a query. I was subsequently given three reference numbers to cite in the process of being shuttled between different departments at the pathology lab. This in addition to my customer number and the invoice number that was the source of my initial query.
Each time I was forwarded to another lab representative, I had to give this admin personal details, describe my query, explain who had forwarded me, and why. Sound familiar?
I know I’m not alone in this digital version of Kafkaesque bureaucracy. I have to ask: why would any company want to subject its customers to such friction? In an age when customer experience is all-important, and companies like pathology labs have everything they need to create a unified view of the customer, it doesn’t make sense. All too often, organisations—even those claiming to be customer-centric—suffer from data fragmentation caused by data silos.
A data silo is a collection of data that is held by one part of an organisation, and “isolated from and not accessible by other parts of the organisation.1” Proprietary software is one of the greatest culprits, and in the past organisations have reverted to paper systems and data-capture personnel (or customised middleware) to move information between silos.
Find out more about HPE Unified DataOps, intelligent data management as a service, from CDW.
Keeping an eye on the bottom line? HPE Financial Support (HPEFS) help ease the pressure with GreenLake.
As the volumes of data being collected by business—from the edge to the core—grow exponentially to astronomical sizes, artificial intelligence (AI) promises great power in the form of useful insights into external and internal aspects of the organisation. External insights, like customer behaviour, can help inform future buying trends; internal insights will help uncover business processes that can be improved, or ways of bringing products to market faster.
But this power can only be unlocked if the organisation has a data infrastructure that can be mined by AI. And there is one main obstacle that stands in the way of this: data sprawl, caused by data silos.
An IDC research white paper, entitled “The Data-Forward Enterprise: How to Maximise Data Leverage for Better Business Outcomes,” found that more than 80 percent of IT leaders surveyed identify data sprawl as one of the most critical problems their organisations must address today2. With data volumes growing incrementally—by some estimates more than doubling every two years—IDC analysts predict that the challenge of managing data sprawl will become increasingly complex.
Most organisations work with multi-cloud environments, with data spread across multiple physical locations and different repository types. The result is that data is often duplicated and fragmented. Research into enterprises shows that all but the most digitally transformed organisations are challenged by data sprawl. In some organisations, customer data is duplicated across different systems, and in manufacturing companies even part numbers may not be matched between logistics and accounting, for example.
The situation is further exacerbated by the proliferation of unstructured data: processes as diverse as diagnostic imaging, modelling, simulations, LIDAR, GIS, genetic sequencing and video production all centre on unstructured data. This is only set to grow as more IoT devices come online, providing unstructured data from the edge.
While Covid-19 was a test of companies’ preparedness for remote work, the coming data tsunami will be a test of their preparedness for managing data. And the numbers don’t look good. The IDC report also found that:
The message is clear: for organisations to remain competitive, they must seek a suitable cloud-first solution that provides security, redundancy and compliance. At the same time, they must use this to create customer journeys that are frictionless and a delight.
On the back end, to solve the problem of siloed data, we turn (unsurprisingly) to AI. Using machine learning (ML), data scientists can create algorithms that allow AI engines to parse these large datasets and mine them for relevant data. The algorithms are designed to interrogate data across different databases, as well as unstructured sources, to extract information that can be analysed for patterns and used for deep learning.
HPE and CDW collaborate with several third-party companies to provide bespoke solutions for our customers. Along with the analytical software, specialised hardware is required: processing power in the form of multi-core GPUs and large, SSD-based storage that can load huge datasets into memory at speed, in order for the algorithms to do their work and present insights quickly.
HPE and CDW provide a revolutionary way of managing siloed data using AI. The result of extensive research into enterprise data systems, Unified DataOps reimagines data and infrastructure management, drives agility and innovation, spans across clouds and empowers organisations.
The Unified DataOps vision introduces a new data experience that eliminates silos and complexity to accelerate data-driven transformation. By combining data-centric automation, cloud-native operations and AI-driven intelligence, Unified DataOps radically streamlines data and infrastructure management.
Delivered with a unique architecture, Unified DataOps simplifies data and infrastructure management from edge to cloud—and enables you to unleash the true potential of your data.
If you don’t put customer experience at the heart of your operation, someone else will. Today, being a data-driven company with unified data operations is a table-stake, not a ‘nice-to-have’. It is a strategic competitive advantage that fuels greater successes through innovation and ever iterating on the customer experience.
Find out more about HPE Unified DataOps, intelligent data management as a service, from CDW.
Keeping an eye on the bottom line? HPE Financial Support (HPEFS) help ease the pressure with GreenLake.
Oreste Majeli - HPE Business Development Manager at CDW
For any enquiries contact: info@uk.cdw.com | 020 7791 6000
Footnotes:
1Retrieved from Webopedia.
2/3Phil Goodwin/Randy Perry, The Data-Forward Enterprise: How to Maximize Data Leverage for Better Business Outcomes, May 2020. Retrieved from Rubrik.