Why You Can’t Get the Analytics You Really Need
Businesses today are awash in data. According to IBM, we create 2.5 quintillion bytes of data every day — so much that 90% of the data in the world today has been created in the last two years alone.
Yet when it comes to utilizing that data to tackle some of the most urgent business issues that major corporations face – such as predicting customer preferences, enabling business transformation, increasing revenues, and improving risk mitigation -- complex barriers get in the way of making their data work for them.
One common issue is that their data is segmented into different divisions, business lines, or acquisitions. Often this means that data is gathered inconsistently so it can’t be compared across the organization. Or perhaps the data isn’t stored and updated properly.
Here’s a simple example: a major clothing retailer has three different brands offering various price points, yet its customers often overlap. The corporation captures customer information in each and you would think it now has a gold mine of information: when do they go for the low-priced commodity and when do they splurge? When do they shop online and where do they browse in-person? Most importantly, how can we harness that information to create an industry leader that anticipates and meet those customers’ needs?
This brings us to a key challenge for many corporations: their businesses have changed. The retail marketplace is experiencing disruptions rendering malls obsolete and smart phone shopping a millennial norm. The company that can utilize its data strategically across the enterprise will survive in the new normal. Its ability to capture, clean up, compare, and apply the data in revenue-generating ways will be key to its success.
Forward-looking companies are discovering their data is a strategic asset, but the next step is to find ways to monetize it. Competition has compelled companies like the national retailer to get smarter and use its data to identify the intrinsic habits of their customer base.
Additionally, data inconsistency is created when companies make acquisitions and diversify. They inherit new systems and sometimes the internal fiefdoms that do not want to share data across other business units. The result is slow or thwarted access to information, and very little intelligence from the data.
These factors will require companies to reorganize, or at the very least break down the silos among business lines and operational divisions.
Unfortunately, even the smartest data technology experts within a corporation may not have the business objectivity or political clout to persuasively point out the gaps in data, let alone lead a data strategy and governance program to close them. That’s why corporations often need guidance on how to transform their data into business intelligence.
So how do we tackle the complexities of reorganization?
Follow the money? Follow the data!
The first step is to understand that if you are going to treat data as an asset, you need to understand how it passes through a company and create a data supply chain. That is not a technology issue, it is a business imperative. A corporation must address how its data flows through the enterprise, and if there are barriers or gaps, it may require some reorganization.
The reason most companies cannot develop the data supply chain is that they were never set up to work in that model in the first place. Restructuring for success is extremely difficult unless you continue to work through it within the right format.
To accomplish this, we welcome a growing rise in importance for the Chief Data Officer and the Chief Analytics Officer to ensure that a company will control and leverage its data supply chain. Increasingly, their goal is to organize the data supply chain so that data can be free-flowing data throughout the organization and at the same time be secure and well-governed.
This is the idea of a data exchange, which is a more fully evolved and viable supply chain concept to meet a corporation’s future needs. The data exchange implies that data is valuable, should be charged for, and produces increased value over time. These things are then measured and the right models used to capture the worth of the data to the business unit, the cost of the loss of that data, and the value of that data to the market. The next step is to prioritize the availability of the sources and focus on the right business intelligence strategy for your company.
Data governance, data security, and data management are keys to success for an organization that utilizes data-driven strategies, but critical factors such as agility and ability to build self-service business intelligence components are equally important. In future blogs, we will lay out each of these areas covering crucial areas of understanding such as the supply and demand of metadata and how to select what business intelligence value method works best for your business.
Kaygen Inc. is an award-winning global technology solutions provider enabling Fortune 500 corporations to leverage their data as a strategic asset to solve their most pressing business challenges and achieve their goals. Their highly specialized data visionaries and proven technologies drive strategies for clients to utilize their enterprise data from the shop floor to the boardroom and make the right decisions to maximize productivity, efficiency, and profitability.
Data-driven solutions include data strategy and governance, data quality and stewardship, master data management, cloud service, business intelligence & analytics, big data & enterprise integration, and technology resource management.
Kaygen’s excellence has been recognized with awards from leading software providers including Oracle.