Pressure makes diamonds. Boards & Executives are under a lot of financial pressure right now.
The overall cost of data storage has been on the rise in recent years, with businesses across all industries feeling the pressure to keep up with the increasing amounts of data being generated. This has led to a need for more efficient and functional ways to utilize data storage across the business, from the board level to the CEO and CFO, in order to gain a more complete picture of the business and make more informed decisions. Many CFO's are not intimately familiar with the data, nor are they familiar with the overall systems that makeup the companies that the approve these large software invoices for. These implications have created a huge dissonance, a gap, that shelters the technical members of the team who request annualized purchases of spend that erode margins - and moreover creates mountains of tech debt to deploy, manage, and scale these data sets to maximize the true value they contain. This is where todays system is broken. CFO's need a tool, a platform, and a strategy (a maturity model for data across the business) to understand what overlaps, what gaps, and what quality of data exists and how to leverage financial approvals to maximize those overall strategies for their companies. The platform that we are mentioning does not exist in the overall structure of most enterprise companies - and CFO's often rely on their C-level counterparts to do what's right for the company - but the conversation of data/value/gaps/overlaps and most importantly duplications is not happening at the scale that would bring large amounts of savings to the bottom line, and reduces the overall impact of messy or inaccurate data at scale. CFO's reading this... Are you aware that this problem also extends to every single investment you're making in AI today? Are you aware of the downstream costs of training AI, building models, and the overall problems (and costs) that mount when the inputs (the data) is not setup for success? This is the pivotal point. If you're reading this and asking yourself these questions as well - then its time to put a plan together. 1) How do you know what the overall accuracy of your data is across your enterprise? 2) What data/fields or overlaps exist within the software/tech/platforms that you're paying for? 3) Do you have a Technical counterpart that knows these answers and how to fix it? ... and finally - the biggest question all that needs to be asked right now by CFO's is:
Are you funding the build out of AI without this foundation today?
Asking these questions is incredibly important to the future impact that these financial approvals will have on your company going forward. Of note, one of the main challenges with data storage vs compute strategies for CFO's is the sheer amount of data that is being generated on a daily basis by your company and where it goes - let alone how (often) its used. Consider this as well - with the rise of the internet of things (IoT) - more and more devices are connected to the internet and generating massive amounts of data (or metadata), which needs to be stored and managed. This has led to a significant increase in the amount of data that businesses need to consider in their operations. Which in turn has led to an increase in the overall cost of data storage... but the good news is that the economics of all of this have shifted in the direction of "compute modeling" - where we pay for what we use. This is a huge gain in the leverage that CFO's now have in asking questions about the systems that they are approving for purchase, where the data goes, and how its used (historical vs real time value).
Value. Interesting word. Interesting strategy. If you're not a technical CFO - then this is the word of choice for you to use as an open ended question when doing the due diligence of purchases going forward - especially with SaaS platforms that have huge amounts of waste and utilization issues when calculated against a) provisioned users b) active users and c) inactive users (usually defined as outside of 90 days of usage).
What additional data correlation value does X platform provide us. Can you show me? Asking these types of questions will cause your enterprise data teams and technology manager to start collaborating more and more toward the vision you have for maximized financial outcomes from data-driven investments. How many other platforms do we have where the data fields match up? Can you show me?
To address these questions - your teams in the data layer will likely take on the challenge - and over a long period of time for your approvals - the culture shifts towards a maximization of data strategy that comes from the signature authority. In the end you get a centralized financial management strategy toward business data investments that helps bolster the bottom line and drive overall effectiveness of the spend of every dollar and the inputs - output relationships that the data has across your enterprise assets (people, software, hardware, platforms, partners, and even the machine learning models and ai being developed). This critical need that businesses have to utilize their data (storage and compute) in a much more functional way across the organization - is going to drive the next generation of healthy businesses and the evolution of their ability to execute on an an AI-driven, automated at scale, successful business. We call this a "financially healthy business" and the results of these processes and maturity discussions about the advancement of data into the Financial Investment layer means implementing more-efficient data management strategies, cost-effective data warehousing and data mining, and a much more "ordo ob chao" - order of chaos - in order to gain insights from the data that is being stored and computed. It also means making sure that all relevant stakeholders, including the board, CEO, and CFO, have access to the data and can use it to make more informed decisions. Chaos controlled.
One other way that many companies are starting to see this advance in a collaborative manner - is Data Governance. This is a great way to do this, and it starts by implementing a data governance strategy (or council), which involves establishing clear roles and responsibilities for data management and ensuring that all stakeholders have access to the data they need. This can help to improve data quality and ensure that the data is being used effectively across the organization. Note: If you take this path, its wise to include someone from the financial team to begin to have a champion for the overall adoption of the methods and mindset shift listed above.
In addition to implementing a data governance strategy, businesses can also benefit from using data analytics tools and techniques to gain insights from the data that is being stored. This can help to identify trends and patterns in the data, and can provide valuable information that can be used to make more informed decisions. At Snowfire - We suggest Data Fusion AI (for data engineering, modeling and correlation at scale) and Generative BI (for business intelligence that does not require as many humans to bring insights, actionable intelligence, and data-driven decisions to the table) - This is the way.
Overall, the cost of data storage is an increasingly important issue for businesses of all sizes. By utilizing data storage in a more functional way across the organization, businesses can gain valuable insights from the data they are storing and make more informed decisions. This can help to improve efficiency, reduce costs, and ultimately drive business growth and success.
