How do we put an economic value on Big Data initiatives in our organizations? How can we measure the impact of such projects in our businesses? How can we convince senior leadership to continue and increase their investment? Today on our blog we share our perspective.
Most of us who are familiar with the Big Data boom, are also familiar with the big and bold promises made about its value for our economies and society. For example, McKinsey estimated in 2011 that Big Data would bring $300bn in value for healthcare, €250bn for the European Public Sector and $800bn for global personal location data. Recently, McKinsey also published an estimation of what percentage of that originally identified value has become a reality as of December 2016, which is up to 30%, with an exception of 50-60% for location-based data.
These astronomic numbers have convinced, and are still convincing, many organizations to start their Big Data journey. In fact, only recently Forbes and IDC have estimated the market value for Big Data and Analytics technology to grow from $130B in 2016 to $203B in 2020.
However, these sky-high numbers do not tell individual companies and institutions how to measure the value they generate with their Big Data initiatives. Many organizations are struggling to put an economic value to their Big Data investments, which is one of the main reasons why so many initiatives are not reaching the ambitious goals they once set.
So how can we put numbers on Big Data and Analytics initiatives? From our experience here at LUCA, there are four main sources of economic value:
Reducing costs with Big Data IT infrastructure
There are considerable savings to be made on IT infrastructure:
from propriety software to open source. The traditional model of IT providers of Data Warehouses is to charge a license fee for the software part and charge separately for the needed professional services. Some solutions, in addition, come with specific hardware.
from propriety software to open source. The traditional model of IT providers of Data Warehouses is to charge a license fee for the software part and charge separately for the needed professional services. Some solutions, in addition, come with specific hardware.
Before the age of Big Data this model had worked well, but with the increasing amount of data (much of which is non-structured and real-time), existing solutions have been come prohibitively expensive. This, in combination with a so-called “vendor lock-in” (due to committed investments and complexity, it becomes very costly and hard to change to another vendor solution) has forced many organizations to look for alternative, more economical, solutions.
The most popular alternative is now provided by the Open Source Hadoop ecosystem of tools to manage Big Data. Open Source software has no license cost, and is therefore very attractive. However, in order to be able to take advantage of the Open Source solutions for Big Data, organizations need to have the appropriate skill set and experience available, either in-house, or outsourced.
The Hadoop ecosystem software runs on commodity software, scales linearly and is therefore much more cost effective. For those reasons many organizations have substituted part of their propriety data infrastructure with Open Source, potentially saving up to millions of euros annually. While saving on IT doesn’t give you the largest economic value, it is relatively easy to measure in the Total Cost of Ownership (TCO) of your data infrastructure, and therefore it is a good strategy to start with.
Optimization of your business
There is no questioning that Big Data and Analytics can improve your core business. There are two ways to achieve such economic benefits: by generating additional revenues or by reducing costs.
Generating additional revenues means doing more with the same, or in other words, using Big Data to generate more revenues. The problem with this is that it is not easy to decide where to start, and it can be hard to work out how to measure the “more”.
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| Figure 2: Measuring the monetary value of Big Data in different areas of your organization. |
Reducing costs means doing the same with less, or in other words, using Big Data to make business processes more efficient, while maintaining the same results.
External Data Monetization
Here, the economic value of Big Data is not generated from optimizing your business, but it is generated from new, data-centric, business. This is only for organizations that have reached a certain level of maturity in Big Data. Once organizations are ready to materialize the benefits of Big Data to optimize their business, they can start looking to create new business around data, either by creating new data value propositions, i.e. new products where data is at the heart, or by creating insights from Big Data to help other organizations optimizing their business. In this case, measuring the economic value of Big Data is not different from launching new products in the market and managing their P&L.
We believe that in the coming three to five years, the lion share of the value of Big Data will come from business optimization, that is, by turning companies and institutions into data-driven organizations that take data-driven decisions. And those are the kind of Big Data initiatives that organizations struggle to put an economic value on.
Savings from IT are a good starting point, but will not scale with the business, while revenues from data monetization will become huge in the future, but are currently still modest compared to the potential value that can be generated from business optimization.
Most businesses start their Big Data journey the right way. They make an opportunity-feasibility matrix, which plots the value of a use case against how feasible it is to realize that value. Figure 2 shows an example from EMC. The use cases to select would be those in the upper right quadrant:
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| Figure 3. Opportunity Matrix for Big Data Use Cases – value versus feasibility. |
A good way to estimate the business value of a use case is to multiply the business volume with the estimated % of optimization. For instance, if the churn rate of a company is 1% (per month) and there are about 10M customers, with an ARPU (average monthly revenues) of €10, then the business volume amounts to €1M per month or €12M per year. If Big Data could reduce the churn rate by 25%, that is, from 1% to 0.75%, then the estimated value would be €250.000 per month. As an example of a cost saving use case, consider procurement. Suppose an organization spends €100M on procurement every year. Analytics might lead to a 0.5% optimization, which would amount to a potential value of €500.000 per year.
There are hundreds of Big Data use cases and the TM Forum gives an extensive overview of some of the most relevant ones in the telecommunications sector.
However, once the initial use cases have been selected, how should you measure the benefits? This is all about comparing the situation before and after, measuring the difference, and knowing how to extrapolate its value if it were applied as business as usual. Over the years, we have learned that there are two main issues that make it hard to measure and disseminate the economic impact of Big Data in an organization:
- Big Data is almost
never the only reason for an improvement. Other business areas will be involved and it becomes then hard to decide how much value to assign to Big Data. - Telling the whole organization and top management about the results obtained. Giving exposure to the value of Big Data is fundamental in raising awareness and creating a data-driven culture in your company.
With regards to point 1, Big Data is almost never the only reason for creating value. Let’s consider the Churn use case, and assume you use Analytics to better identify what customer are most likely to leave in the next month. Once the customers have been identified, other parts of the company need to define a retention campaign, and yet another department executes the campaign, e.g. through calling the top 3000 people at risk. Once the campaign is done, and the results are there, it is hard to decide whether the results, or what part of it, are due to Analytics, due the retention offer or due the execution through the call centres.
There are two ways to deal with this issue:
- Start with use cases that have never been done before. An example of such a use case would be to use real-time, contextual campaigns. Real-time campaigns are not yet frequently used in many industries, and require Big Data technology. Imagine you are a mobile customer with a data tariff, and watching a video. The use case is to detect in real-time that you are watching a video and that you have almost reached the limit of your data bundle. The usual things to happen in those cases are that you either are throttled or are completely cut-off from Internet. Either situation results in a bad customer experience. In the new situation, you receive a message in real-time telling you about your bundle ending, and asking you whether you want to buy an extra 500MB for €2. If you accept this offer, then in real-time the service gets provisioned and you are able to continue watching your video. The value of this use case is easy to calculate: simply take the number of customers that have accepted the offer and multiply it by the price charged to the customer. Since there is no previous experience with this use-case, few people will challenge you that the value is not due to Big Data and Analytics.
- Compare with what would happen if you didn’t use analytics. The second solution is a bit more complex, but applies more often than the previous case. Let’s get back to the churn example. It is unlikely that an organization has never done anything about retention, either in a basic or more sophisticated way. So, when you do your Analytics initiative to identify customers that are likely to leave the company, and you have a good result, you can’t just say that all is due to Analytics. You need to compare it with what would have happened without Analytics, all other things being equal. This requires using control groups. When you select a target customer set for your campaign, you should reserve a small, random part of this set to treat them exactly the same as the target customers, but without the Analytics part. If you do so, then any statistically significant difference between the target set and the control group can be assigned to the influence of Analytics. For instance, if with this, you retain 2% more customers than the control group, you then calculate how much revenue you would retain annually, if the retention campaign would be run every month. Some companies are able to run control groups for every single campaign, and so are always able to calculate the “uplift”, and thus continuously report the economic value that can be assigned to Analytics. However, most companies will only do control groups in the beginning to make and confirm the case, and once confirmed they consider it business as usual (BAU), and a new baseline has been created.
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| Figure 4: Sharing the impact of Big Data in your organization is fundamental. |
With regards to point 2, sharing results of Big Data within the organization in the right way is fundamental. It is our experience that while business owners love Analytics for the additional revenues or cost reduction, at first they are not always willing to tell the rest of the organization about it. But evangelizing in the organization about the success of the internal Big Data projects is critical to get top management on board and to change the culture.
Why would individual business owners hesitate in sharing? The reason is as simple as it is human. Showing the wider organization that using Big Data and Analytics creates additional revenue makes some business owners worry about getting higher targets, but not with more resources (apart from Big Data). Similarly, other business owners might not want to share a cost saving of 5%, since it might reduce their next budget accordingly. Haven’t they shown – through Big Data – that they can achieve the same goals with less? This is an example of a cultural challenge. Luckily it is not sustainable to maintain such a situation for a long time, and in the end, all organizations get used to publishing the value. But, it might be a problem especially at the beginning of the Big Data journey, when such economic numbers are most needed.
For those organizations that in the end do not succeed to measure any concrete economic impact, don’t worry too much either. Experience teaches us that, whereas organizations at the early phase of their journey are obsessed with measuring value, more mature organizations know that there is value and do not feel the need anymore to measure improvements. Taking full advantage of Big Data has changed the way departments interact and that is one of the main value drivers. Big Data has become fully integrated with Business As Usual. Big Data = BAU.
Companies, government agencies and other organizations accumulate huge amounts of a wide variety of data – about the market, customers, regulations and policies, projects, equipment, performance indicators, etc. This information contains a huge potential and a lot of opportunities, but it is not always used in full. Also it is necessary to understand that almost three quarters of such information, as a rule, is unstructured and unordered.
How can you "read" all this information, see the main thing and find patterns? Big Data technologies come to the rescue, which are characterized by special speed and accuracy. In this case, the Big Data feature is caused not so much by large volumes as by the inability of the old methods to quickly cope with the flows of a variety of data coming from a huge number of external and internal sources having different structure and indexing schemes.
Today, it is important for organizations to find the best way to extract useful and relevant information from their data in the shortest possible time. ActiveWizards company will help not only to find previously hidden patterns, trends and relationships, but also to receive this information in real time and as a result, to make faster management decisions, based on qualitative data.