Why Every Business Needs A Data Strategy
Why Every Business Needs A Data Strategy.
Today’s most valued commodity, and one of the most prized assets of any business, is not traded on any mercantile exchange. At least not like other precious commodities. Yet many of the most valuable companies in the world have successfully leveraged this commodity to build and expand their businesses. This commodity, the most valuable asset of just about every business, is data.
Data as an invaluable commodity has surpassed the value of other precious commodities such as oil, gold, etc. Google, Amazon, Facebook, are three obvious examples of innovative companies that leverage the data they collect on each of us - transform it into knowledge (in most cases wisdom) - exploiting their data resources to achieve massive market valuations.
They are not alone though. There are numerous examples outside of pure data plays. Domino’s transformed itself into a company built on collecting and leveraging data about its customers. Shipt, a grocery delivery gig-market play, lives and dies on data about its partner groceries. Every major bank in the world relies on data to market to customers, prevent fraud and maximize investments. Data has literally become the lifeblood of the modern business.
As technology has evolved across all aspects of business, it’s become a critical component of any organization’s business model. Having a sound and agile process for managing your business plan with an aligned technology strategy is critical to success. It is equally critical, if not more so, to have a comprehensive Data Strategy. The proliferation of data across the company through the deployment and use of technology has exploded and has rapidly become the critical asset of the business.
Anyone who’s worked in the banking world or in healthcare knows about the regulations and requirements around managing PHI, PCI and PII data. As more businesses bridge the gap and establish a direct relationship with their products’ end users, the personal data they’re collecting is rapidly multiplying, and they’re having to meet these compliance protocols within their data governance and management programs.
Europe is rapidly expanding these regulations and requirements to all businesses that maintain personal information about their customers — which is, basically, all businesses. The US is beginning to follow suit. The state of California has implemented new privacy laws that come into effect starting January 2020. Your Data Strategy, therefore, cannot just be about the leveraging of data to increase business value, but the defense of that data to safeguard business value.
Equally important is a strategy for archiving data, by offloading it from real-time transaction processing systems and building a data lake that will support data analytics. Given the exponential growth in data, many systems were designed to support the needs of the transaction system they serve. Most use a RDBMS which, at the time of build, was the best option for rapid transaction processing and query analysis.
Over the past two decades, these systems have accumulated extensive data stored on large databases. In many cases, no allowance was made for archiving this data, leaving many companies have a wealth of data that’s not being effectively leveraged analytically, and is cluttering up the RDBMS supporting the transaction system.
Those companies that do have archiving strategies mostly designed them for efficient data-store management, and not necessarily for analytical purposes. The data archived was not massaged in any fashion for analytical use, but was designed to keep the data-store efficient for the transaction process — with the archived data placed somewhere in a format that would enable it to be easily restored, should it become necessary for auditing or other transaction-supporting activity.
Many of these systems viewed data’s function as purely to support the needs of transactions. Its value historically was perceived as being for a specific period. Once a transaction was completed, or a certain period in time had elapsed, the value of the data diminished. That’s changed significantly in the past 2 decades, as we’ve grown to understand how to use this data to better serve our customers, make informed business decisions, and transform data into information — and information into wisdom.
Here are 5 key considerations when setting up a sound data strategy.
1. IDENTIFYING AND UNDERSTANDING
Identify and know your data. Put a Data Dictionary in place. Identify elements that pose a security or compliance risk.
Most of our clients ask us what data they should keep. You should keep any data you can identify. What may seem meaningless today may become valuable in the future. Data storage today is very affordable in archives such as S3.
Additionally, all users should have only the minimum required permissions to accomplish their jobs, and this should be enforced at the IAM policy level.
2. DATA STORE
Define the data-lake storage you’ll use for storing the data. Identify the storage model, and document it in a Data Dictionary. Massage and dress-up the data as you store it. Don’t think about transactions or RDBMS, but rather analytics. Remove unnecessary complexity through Referential Integrity and place the referenced data into the current record being created. All of this will make analytics much faster and more robust.
3. DATA GOVERNANCE AND COMPLIANCE
Having a data strategy in place will enforce the requirements and policies for sound data governance. Make sure you constantly review these policies and procedures, and update them regularly as the data store grows and additional security elements get added to the store.
Provision your data in a way that enables your data analytics team to access it. Ensure that it’s made available in the most efficient manner within the allowable security and compliance guidelines, as established by your data governance policy and procedures.
In AWS, you should implement Inspector — first, to scan each of your EC2 instances for vulnerabilities, then to report on those for correction by your DevOps team. This handles the server-side for vulnerability scanning. You should also implement Config, which scans AWS resources, in general, for vulnerabilities created by the ways you’ve provisioned and configured them. Those two safeguards, properly implemented, will give you a complete vulnerability picture across your AWS infrastructure.
5. ANALYSIS AND PROCESS
Process/analyze your data, with a plan and strategy to extract value from the repository. Be agile in your analysis. While data analysis is meant to answer questions and provide guidance or justification, it routinely opens-up new ideas, addressing questions you haven’t previously thought of — which may result in more questions than answers. This is the new horizon you’ll encounter in your information. The further you dig, the more informed you’ll become — which should result in better knowledge leading to not only predictive analytics but prescriptive analytics.
Many businesses want to understand how they can monetize the data they have. A sound Data Strategy coupled with a strong data analytics team will ensure that you will monetize this valuable commodity. It will lead to a deeper understanding of trends in your markets, resulting in better products and services for your customer, while giving you a competitive edge.
As your data goes through its many forms of transformation — and builds out to information and knowledge — your marketing, sales and customer support teams will grow in wisdom that enhances your business and serves your customers better. This, in turn, will help you identify new markets and opportunities, and build better products to address these markets. In short, your data will become what it should have been all along — the lifeblood of your business.