Month: October 2019

IBM just released the results of a global study on how businesses can get the most value from Big Data and analytics. They found nine areas that are critical to creating value from analytics. You can download the entire study here.

IBM Institute for Business Value surveyed 900 IT and business executives from 70 countries from June through August 2013. The 50+ survey questions were designed to help translate concepts relating to generating value from analytics into actions.

Nine Levers to Value Creation

The researchers identified nine levers that help organizations create value from data. They compared leaders (those who identified their organization as substantially outperforming their industry peers) with the rest of the sample. They found that the leaders (19% of the sample) implement the nine levers to a greater degree than the non-leaders. These nine levers are:

1. Source of value: Actions and decisions that generate results. Leaders tend to focus primarily on their ability to increase revenue and less so on cost reduction.
2. Measurement: Evaluating the impact on business outcomes. Leaders ensure they know how their analytics impact business outcomes.
3. Platform: Integrated capabilities delivered by hardware and software. Sixty percent of Leaders have predictive analytic capabilities, as well as simulation (55%) and optimization (67%) capabilities.
4. Culture: Availability and use of data and analytics within an organization. Leaders make more than half of their decisions based on data and analytics.
5. Data: Structure and formality of the organization’s data governance process and the security of its data. Two-thirds of Leaders trust the quality of their data and analytics. A majority of leaders (57%) adopt enterprise-level standards, policies and practices to integrate data across the organization.
6. Trust: Organizational confidence. Leaders demonstrate a high degree of trust between individual employees (60% between executives, 53% between business and IT executives)
7. Sponsorship: Executive support and involvement. Leaders (56%) oversee the use of data and analytics within their own departments, guided by an enterprise-level strategy, common policies and metrics, and standardized methodologies compared to the rest (20%).
8. Funding: Financial rigor in the analytics funding process. Nearly two-thirds of Leaders pool resources to fund analytic investments. They evaluate these investments through pilot testing, cost/benefit analysis and forecasting KPIs.
9. Expertise: Development of and access to data management and analytic skills and capabilities. Leaders share advanced analytics subject matter experts across projects, where analytics employees have formalized roles, clearly defined career paths and experience investments to develop their skills.

The researchers state that each of the nine levers have a different impact on the organization’s ability to deliver value from the data and analytics; that is, all nine levers distinguish Leaders from the rest but each Lever impacts value creation in different ways. Enable levers need to be in place before value can be seen through the Drive and Amplify levers. The nine levers are organized into three levels:

1. Enable: These levers form the basis for big data and analytics.
2. Drive: These levers are needed to realize value from data and analytics; lack of sophistication within these levers will impede value creation.
3. Amplify: These levers boost value creation.

Recommendations: Creating an Analytic Blueprint

Next, the researchers offered a blueprint on how business leaders can translate the research findings into real changes for their own businesses. This operational blueprint consists of three areas: 1) Strategy, 2) Technology and 3) Organization.

1. Strategy

Strategy is about the deliberateness with which the organization approaches analytics. Businesses need to adopt practices around Sponsorship, Source of value and Funding to instill a sense of purpose to data and analytics that connects the strategic visions to the tactical activities.

2. Technology

Technology is about the enabling capabilities and resources an organization has available to manage, process, analyze, interpret and store data. Businesses need to adopt practices around Expertise, Data and Platform to create a foundation for analytic discovery to address today’s problems while planning for future data challenges.

3. Organization

Organization is about the actions taken to use data and analytics to create value. Businesses need to adopt practices around Culture, Measurement and Trust to enable the organization to be driven by fact-based decisions.

Summary

One way businesses are trying to outperform their competitors is through the use of analytics on their treasure trove of data. The IBM researchers were able to identify the necessary ingredients to extract value from analytics. The current research supports prior research on the benefits of analytics in business:

1. Top-performing businesses are twice as likely to use analytics to guide future strategies and guide day-to-day operations compared to their low-performing counterparts.
2. Analytic innovators 1) use analytics primarily to increase value to the customer rather than to decrease costs/allocate resources, 2) aggregate/integrate different business data silos and look for relationships among once-disparate metric and 3) secure executive support around the use of analytics that encourage sharing of best practices and data-driven insights throughout their company.

Businesses, to extract value from analytics, need to focus on improving strategic, technological and organizational aspects on how they treat data and analytics. The research identified nine area or levers executives can use to improve the value they generate from their data.

For the interested reader, I recently provided a case study (see: The Total Customer Experience: How Oracle Builds their Business Around the Customer) that illustrates how one company uses analytical best practices to help improve the customer experience and increase customer loyalty.

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Buy TCE: Total Customer Experience at Amazon >>

In TCE: Total Customer Experience, learn more about how you can  integrate your business data around the customer and apply a customer-centric analytics approach to gain deeper customer insights.

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In a recent survey of 500 application teams, predictive analytics was the number one feature being added to product roadmaps. It’s clear why: Predictive analytics solves critical business challenges and adds tremendous value to applications.

>> Related: How to Package and Price Embedded Analytics <<

As application vendors begin to add predictive insights into their applications, the question becomes: How should they price predictive capabilities? Let’s look at the best (and worst) ways to price and package predictive analytics with your product.

The Wrong Way to Price Predictive Analytics

Say you have a cloud-based customer churn reporting application, and your customers pay a $500 per month subscription to use it. Then you add predictive features as a new module. How will you price that module?

A common predictive analytics pricing strategy is to choose a price point—such as 30 to 50 percent—and add that on top of the current monthly license. If we pick 50 percent, then the new module will be priced at $250 per month. But is that a good way to price your product?

Your price should be based on supply and demand as well as the value your product offers. A typical dashboard application is already a commodity, so it is difficult to charge a premium since many vendors offer similar application functionality. On the other hand, predictive analytics is a hot technology. It provides unique insights into the future, and few vendors have incorporated it in their products. Those who roll out predictive analytics features first will have a head start in capturing the market.

So, even though it may seem like a 50 percent markup is reasonable, that doesn’t get to the heart of the value of predictive analytics. The answer to our earlier question is no: Basing the price of a new premium feature (predictive analytics) off of your commodity features (embedded analytics) is not a good way to price your product.

The Right Way to Price Predictive

If the traditional pricing strategy is out, how do we price your predictive application? Since our new module can predict who will churn, let’s identify what value the module offers for a customer. Say a customer is losing one million dollars annually due to churn and is looking to reduce that by 20 percent a year. By identifying who is likely to churn and taking a proactive approach, the predictive module can help your customer save $200,000 a year—that’s a $200,000 value!

So, how much can you charge a customer for that type of value? Fifteen to 30 percent is reasonable, but let’s be very conservative and say you only charge 10 percent. That’s a price of $20,000 per year (or $1,667 per month) for the predictive module.

Remember, your current churn dashboard costs customers $500 per month. We’ve given the new predict module a starting price of $1,667 per month—that’s three times higher than your current commodity dashboard pricing. This may seem like a lot, but it’s your marketing department’s job to create a strong sales pitch that clearly conveys the value to your customers.

The Best Way to Price Predictive: Think Outside the Box

Is there something we can do to make the predictive analytics pricing (and the total application package) more appealing to customers? Yes, there is!

Since the new predict module is adding much more value than your dashboard reporting application, your product manager should lead all sales with it. It doesn’t make sense to sell your customers on the old application and then mention the optional predictive module later on. The new predictive application is going to be the most valuable to customers, so make it the focus of the application. The best way to price predictive analytics is to create a new package for, say, $2,000 a month. This should include everything: your commodity dashboard application and your new predictive capabilities.

In summary: Push yourself to calculate the value your product offers. Challenge your marketing team to figure out innovative ways to articulate that value. And finally, come up with forward-thinking packages that lead with your predictive functionality.

See how Logi can help with your next predictive analytics project. Watch a free demo of Logi Predict today >

 

Originally Posted at: How to Price Your Predictive Application by analyticsweek

90% of the world’s digital data has been generated over the past two years. This data is now being used to solve issues across the globe. We take a look at big data usage throughout the United States, China, Indonesia, France, Philippines, Norway, Spain, and Brazil:

Created by Mushroom Networks

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We are hearing all the hoopla about Big-data. How it is radically changing the way we look at company data and provide data driven reasoning for better and less risky decision making. Innovation is one such area. Big-data could provide a real lift to DDI. Having a data driven approach will help in better, targeted and relevant innovations that are craved by the clients/ customers. This bottom-up approach at its most effective form could be easily conceived by a good data driven innovation strategy.

Now let’s get into the primer on DDI- What is entails and how could one leverage that. Here is the Who, What, Where, Why and When of Data Driven innovation.

Why use DDI?
Let us address why do we actually need to use DDI- what will it buy us. Consider a situation where you have to come up with your next best product/feature/innovation. Where would that come from? From your gut based on some hunch or from hardcore actual data from right sources. Hunch based discoveries are great but their failure rate is higher. Also, they are difficult to validate as the implementer has to do various focus groups which in themselves are flawed to certain extent. Now consider a case where your product-customer interactions, operations fill you up on what is relevant and you can use data to understand its impact on the organizations. This helps you identify what matters most and helps you choose the idea with substantial data to back up the theory. So, there is no need for spending money on focus groups, but there is a need to leverage real interactions with the real customers/people leading to real results. This ultimately means lesser chance of failure and cost effective way to find the next big thing that is most craved by your customer or organization. This reduces the risk of failure substantially and puts you at ease. So, DDI is important and it could provide a sustainable and continuous way to innovate, iterate and improve.

What is DDI?
Data driven innovation, as name suggests is the way through which data is used for learning about new features, modifications, product ideas that is most cherished by your current customers, market landscape. However, its usage and manifestation in an organization could be different based on its structure, maturity, usage and implementation. Its definition could very well incorporate the application and purpose it is set to achieve. For some organization DDI is a way for finding process improvements, for others it is way to learn from customers and how they use products to learn about next features and/or products, for some it is a sustained source of learning about people, process and technology. But, I would put it in generic and call it “A method to innovate/iterate/improve using sustainable & continuous ways using data based decision process, where data is sourced to help learn about people, process and technology critical to your organization”.

Who could use DDI?
Data driven innovation is not everyone’s play. Not that it is too difficult to implement or it requires too much investment, but it requires certain maturity in your data handling capability before getting started. If you are diligent about using data to learn about your processes and its effectiveness, it will be easier. If you are not yet focused towards using data around your product and processes, you still might have some distance to travel before you delve into data driven methodologies. It is never late to start planning and executing strategies to introduce and leverage data points that go beyond your traditional direct customer & product data. So, in short, DDI could be used by any organization that is serious about learning from data. In Fact, smaller the firm, the better it could be implemented and lesser it would cost. The more silos, more complicated product/process structure, the more it is going to cost, to execute. In short, you could safely tag your DDI initiative on your management, the more selling your management requires for a data driven project, the farther you are from pursuing a full scale DDI. So, first get the leadership buyin on its value and then start shaping your organization to implement DDI.

Where will DDI take place?
Yes, you could figure out that DDI is a system that runs on data driven insights and data is everywhere in an organization so, it could show up anywhere. But, it is a bit trickier than that. The toughest part is not when data driven decision making is running in an organization’s DNA but the time when organizations decides to get started. DDI requires some careful understanding of how data works and how it could be used to get insights. Therefore, place where it should start is important. The best starting place for DDI could be around project managers, or if your organization is big enough to accommodate project management office, for agile companies, it should be around group leads. In short, DDI should start from a place that is not a stranger to data and understands how to handle it. So, in short, it could exist everywhere but it should start from a place that provides the most amiable surroundings required by a data driven project. Project managers, supervisors, PMOs are meant to keep a tap on the progress, so they possess some basic skills to function as data driven professionals and therefore, could help the best in understanding and executing a good DDI strategy.

When is the time to delve into DDI?
In short, the sooner the better. DDI requires substantial amount of preplanning and dedication. The sooner organizations delve into data driven innovation, the better will be its execution and value to the organization. A good data driven innovation implementation requires some practice and iterations on data models, validations, analysis and reporting. So, a successful implementation will rarely emerge from first implementation and would require some iteration. Also, the sooner the organization will start in direction of implementing DDI, the better it is because organizations will start acting in ways to facilitate smart data handling, which will have its own benefits. But, one caveat is that organization should have data to play with. Doing DDI sooner when data handling capabilities are not established could confuse the processes and steer the implementation in wrong directions. So, we could reword our sooner as “the sooner the organizations have started embracing data based decision making process, the better”.

To summarize, DDI is important and beneficial to any organization. It has the tendency to make any organization grow sustainably without having to invest too much into research and development. It support continuous improvements and that too without investing too much money and it could re-utilize the same infrastructure for sustainable leanings.

As a treat, here is a video on Big-Data and Innovation:

Originally Posted at: Data Driven Innovation: A Primer by v1shal