In our 2021 research, we discovered that 33% of firms have no budget set aside for data governance. A further 37% were only using ad-hoc budgets, usually drawn from other IT initiatives or projects. This normally involves spending money on data cleansing/migration when implementing a new technology rather than investing in a strategic data improvement program. Funding Data Governance is imperative if you want to succeed – take a look at the difference in data maturity scores:
Firms with no data governance budget score 2.1 on average. Those with a dedicated budget (unsurprisingly) score higher – 3.69 out of 5.
Read on to learn how you can get funding for your data governance initiative.
Funding data governance – where to begin
The first step to securing a budget is to identify areas where data is causing pain in your firm today. Nobody will fund an improvement program unless they are aware there’s a problem to be solved. You have 2 different approaches to choose from here:
- If you already have a sympathetic executive that wants to improve data, focus on problems that impact that executive
- Otherwise, you’ll need to align data improvements against business strategies and focus on data that drives the business forward
Once you’ve identified key business activities of importance for your business sponsor, you need to evaluate the data that is supporting these activities. If the data is causing pain today (low productivity, cost overruns, missed revenue etc) then we put a price on that pain to justify our efforts to fix the problem.
Putting a price on data pain
Let’s assume we’re going straight for the holder of the purse strings – the CFO. They have authority and budget and can fund our work easily. How do we identify data pain points that impact the CFO? Let’s explore four ways data can increase company value. Once we know how data can drive value, we’ll be in a better position to see where data erodes value.
Improving free cash flow using data
Cash flow is simply the movement of money in and out of your company bank accounts. When you sell something, cash comes in. When you purchase supplies or pay salaries, cash goes out.
Why is this important? Because there’s often a difference between how quickly we get paid vs how soon we need to pay our bills. If we get paid late, we might not be able to pay our bills on time. When companies mess up their cash flow, they go insolvent.
Where does data cause cash-flow pain?
If we look at business processes that bring cash into the business, the CFO will care about invoice accuracy. In order to create accurate invoices, the finance team rely on timely, accurate data from the business. If it takes the finance team too long to issue invoices then you’re waiting too long to be paid. Worse, if your invoices have errors on them, your customers will likely dispute the invoice. Things to look out for:
- How much does it cost to create an invoice?
- IOFM research pegs the cost between $6.10 and $15.97 for firms producing between 20 and 100,000 invoices manually per year
- How much time does it take to produce an invoice?
- Top firms issue an invoice within 3-4 days. Laggards take up to 17. Each day you delay is another day you won’t get paid.
- How long does it take to get paid?
- Called “days sales outstanding” – under 30 days is good, over 48 and your invoices are a problem
What do we do with this?
If your metrics show that you’re at the back of the pack, take a look at the data used to create invoices. Talk to the Accounts Receivable team and get their feedback on why they think things are slow. Most likely you’ll find data problems – siloed systems, manual processes, ambiguous data. Put a dollar value on these data problems:
- Staff costs – if you are mired in manual processes, you probably have too many people doing the work.
- Work out how much more efficient you can be and put a price on that.
- Working capital costs – if you don’t get paid on time, you need to borrow money to cover your bills
- What is your cost of capital, and how much capital is tied up in data inefficiency?
What else should I look for?
Inventory levels also eat into your working capital. If you’re a business that procures goods to create new products or resell, you need to make sure those processes are optimised. When our Master Data is inaccurate, we may over-order inventory that we don’t need. Inventory sat on a shelf costs money – in insurance, storage and wastage costs as well as tying that money up in assets that are not yet needed. If you have no challenges in this space, move on to the next:
Increasing Revenues with data
Driving up revenue with data will impress the CFO, but also the CEO, Head of Sales and most other C-Suite leaders. There are 2 main methods you can use to drive up revenue:
- Directly sell your data on a data marketplace
- Improve Customer Experience so your customers spend more money with you
If you wanted to directly sell data you need to identify a market for it. Check out data marketplaces to see whether the data your firm captures could be sold. There are now over 4,000 data brokers operating. This is now a $2 billion dollar industry. Chances are you have some data in your organisation that could add value to another firm.
To work out how much your data might be worth, you need to take a look at how much a similar data set is being sold f0r on a marketplace. Once you’ve found something comparable you need to evaluate whether your data is better or worse to determine its value. Imagine you’re online looking at the prices of a second-hand car to work out how much to sell your own car for.
What do you need to consider?
Data value drivers
Here are some key value drivers to consider:
- Usefulness – how useful is your data to someone else?
- Completeness – how much of the total data about this subject do you have?
- How consistent is your data?
- How accurate is the data?
- Timeliness – how up to date is your data, and can you deliver it quickly enough for someone else to benefit from?
- Exclusivity – is your data unique or widely available?
- Are there any restrictions on the use of the data that might impact its value?
- What risks are associated with this data? If there’s a regulatory risk (e.g. GDPR penalty) then it may reduce the value
- What’s the utility of this data? How easy is it to use the data and how interoperable is it with other data or systems?
What do we do with this?
If you’ve identified some data sets or data products that your firm could sell, you can then present this to the CFO (or another leader) as a direct revenue boost. Calculate the cost to improve that data and include it in your business case.
In order to be successful, you’ll need the value from the new revenue to cover your costs. This approach to funding data governance is pretty clear-cut.
Driving up customer satisfaction with data
Happy customers buy more from you and refer you to their friends and family. This has a direct impact on revenue. In order to use this approach, you need to know a few things:
- Does your firm measure Customer Satisfaction today (e.g. CSAT or NPS scores)?
- Do your customers complain about service issues related to bad data?
The best way to capture this info is to grab customer complaints and run direct surveys asking “would you recommend our company to a friend or colleague?”.
If you have internal data, use that, otherwise, you’ll need to do some more work.
If you’re in a B2C business and you are large enough, you can probably find out the answer to these questions using a site like Trustpilot. Real-world customer reviews might look like this:
Using bad customer reviews
The review on this page shows that there are errors in order details, account details, billing issues and contact details. These data issues have angered this customer so much that they resorted to posting a 1-star review online. Imagine what they tell their friends and family about this company?
If you can identify complaints about your firm you’ll know which data needs to be fixed. If you can reduce the number of unhappy customers by 10%, then your revenue will go up by 4.14%.
What do we do with this?
Go and find your current revenue numbers. Make an educated guess about how many negative reviews are related to bad data. If you fixed that data, those complaints would stop coming in. To calculate the value of this, just work out what percentage of reviewers are complaining about data issues.
For example, if you have 10% bad reviews caused by data then removing them will increase your revenue by 4.14% – so calculate that value and put it in front of your CFO. I bet they take notice.
Cutting costs with data
Both of the above examples look at where bad data costs the company money. Accountants waste time creating invoices because the data is bad. Customers call for support and get pushed from pillar to post. Every second wasted on a support hotline is money your firm is wasting.
All business processes rely on high-quality data to operate efficiently. Without good, well-documented data, our teams waste time and effort trying to do their job. Where do you look for that? Survey your colleagues and see where they’re wasting time today.
McKinsey has done this for senior leaders:
About 530,000 days of managers’ time are squandered each year for a typical Fortune 500 company, equivalent to some $250 million in wages annuallyMckinsey Research
Bad data costs for your business
If you can’t pull numbers for your firm, take a look at external research to identify the scale of the problem. As you can see, information workers are wasting 38% of their time finding data. That time is money you’re wasting to get no output. Recently I talked with a data leader in the Government and they ran a study where 77% of their employee time was wasted because of data challenges.
What do we do with this?
You can look at your annual report and identify the cost for staff. If you’re in line with the statistic above, then 38% of that expense is due to time wasted looking for data. Documenting your metadata can reduce this expense by up to 97%, based on our research.
Sometimes this figure is even worse – CFOs of large firms often outsource manual work to cheaper geographies. You need to understand whether this is happening in your firm because that’s “off the books” – but you’re paying that outsourced firm to do your manual data cleansing. Fix the data and the outsourcing cost will disappear.
Reducing risk with data
- Regulatory risk (GDPR fines, for example)
- Cybersecurity risk (both costs of the response and erosion of customer trust)
- Specifically for the CFO – Financial restatement risk
Mishandling Personally Identifiable Information (PII) comes with big penalties these days. Amazon had the dubious honour of taking the top spot for GDPR fines when this was written – copping an $877 million dollar fine.
Calculating how much you may be fined is not straightforward. In order to do so, you need to review all of the regulations your firm is subject to and then identify those that you’re most likely to be in breach of. Looking at the magnitude of fines issued to other, similar firms is the easiest way to demonstrate to management that you are taking on too much risk in your own business.
IBM has been running research on the cost of a data breach for many years. On average it costs an organisation $4.24m USD. Ouch.
Hackers target data that is not protected, but your cybersecurity team can’t be expected to know what to protect unless you’ve given clear directions. Enrich your metadata to keep an inventory of your most critical data, and set expectations about how it should be protected. The IBM research has broken out these risks and quantified the average cost by:
- Company headcount
- Company HQ geography
Grab their research and find out the average cost of a data breach in your firm.
What do we do with this?
Looking through the IBM data will let you pick out the likely cost if your firm was compromised. Your efforts to document data will reduce that risk. Work with your Enterprise Risk Management team to agree on the level of risk reduction you can expect from classifying data.
IBM suggests that organisations with good incident response plans can reduce their risk by up to 74%. Your firm will find it easier to build an incident response plan if you have documented where your PII data resides. This risk reduction figure can now be incorporated into your data governance business case.
Based on our research, this use case will also align nicely with the CIO’s priorities for 2022.
Financial restatement risk
This is the risk that the financial statements of an organization have been misstated to a material degree. The risk comes about both through error and through fraud. Clearly, the examples posed in the Free Cash Flow section also apply to the creation of books and records for the business. If errors exist in your invoices, they could well exist in the journal lines entered into your financial statements too.
Restatements are required when it is determined that a previous statement contains “material” inaccuracy. These inaccuracies are often caused by ambiguity. Kraft Heinz is one example of a firm that was investigated by the SEC for accounting and procurement policy lapses. This led to the firm increasing the Cost of Products sold by $25m. Eventually, the firm needed to restate 3 years worth of financial reports, and their share price tumbled as investors lost confidence in the data they were being asked to value the firm on.
What do we do with this?
Take a look at the manual processes being used to create your books and records today. One major Asian Insurance firm I worked with were posting 520,000 journal lines to their General Ledger each year. 200,000 of these were being manually posted using Excel. This was only in one of their 14 different business units. When 38% of your journal lines are manually created, not only do you have huge staff overhead expense but you also have the massive risk of a material error creeping in.
The cost to present here is the cost of finding and fixing the errors, but also to restate 3 years of financial records. None of this work adds value to your firm, so it’s 100% wasted effort.
Bringing this together
How do you use these data valuations? By identifying key areas in your business where data can drive up revenues/unlock free cash flows or drive down costs and risk, you have something to start a conversation over. Rather than turning up in the CFO office asking for money to improve data, you’re turning up demanding funding money armed with hard facts. Funding data governance is substantially easier when you understand the upside.
Remember, we are not asking for money to be spent aimlessly. We present a clear picture of where bad data practices are wasting money today. Then we document how much money it will cost to improve our data. Provided the cost is less than the benefit your CFO should jump at the chance to fund your data improvement program. You will also have a clear picture of where you’ll start your work and who you need to help along the journey.
Where can I learn more about these techniques?
The concepts above are easy to understand but sometimes tricky to apply. If you are not an expert in finance, risk, compliance and legal terms it can be daunting. That’s why we put together a course specifically teaching these important skills. It’s jam-packed with fail-proof data valuation techniques that you can immediately use in your business.
The course contains:
- 2 hours and 35 minutes of high-quality video training content
- Videos are broken into bite-sized nuggets of knowledge between 3 and 7 minutes long
- A 195-page e-book detailing each technique
- A calculator template that you can use to create bespoke valuations for your firm (for each valuation method)
- Detailed explanations to help you translate the valuations into numbers your board will care about
- 19 lessons
- 35 topics
- 8 quizzes, and
- A certificate to show you’re a data valuation expert
Interested to learn more?
Check out the Cognopia Academy Course: Fail-proof data valuation techniques: Monetise your information assets
The course is priced at $399 USD (bundle pricing is available). It’s a small price to pay if you need funding for your data governance initiative.
Good luck in your ongoing data monetisation journey!