Month: October 2019

When your organization decides to pull the trigger on a cloud migration, a lot of stuff will start happening all at once. Regardless of how long the planning process has been, once data starts being relocated, a variety of competing factors that have all been theoretical become devastatingly real: frontline business users still want to be able to run analyses while the migration is happening, your data engineers are concerned with the switch from whatever database you were using before, and the development org has its own data needs. With a comprehensive, BI-focused data strategy, you and your stakeholders will know what your ideal data model should look like once all your data is moved over. This way, as you’re managing the process and trying to keep everyone happy, you end in a stronger place when your migration is over than you were at the start, and isn’t that the goal?

BI-Focus and Your Data Infrastructure

“What does all this have to do with my data model?” you might be wondering. “And for that matter, my BI solution?”

I’m glad you asked, internet stranger. The answer is everything. Your data infrastructure underpins your data model and powers all of your business-critical IT systems. The form it takes can have immense ramifications for your organization, your product, and the new things you want to do with it (and how you want to build and expand on it and your feature offerings). Your data infrastructure is hooked into your BI solution via connectors, so it’ll work no matter where the data is stored. Picking the right data model, once all your data is in its new home, is the final piece that will allow you to get the most out of it with your BI solution. If you don’t have a BI solution, the perfect time to implement is once all your data is moved over and your model is built. This should all be part of your organization’s holistic cloud strategy, with buy-in from major partners who are handling the migration.

Picking the Right Database Model for You

So you’re giving your data a new home and maybe implementing a BI solution when it’s all done. Now, what database model is right for your company and your use case? There are a wide array of ways to organize data, depending on what you want to do with it.

One of the broadest is a conceptual model, which focuses on representing the objects that matter most to the business and the relationships between them (vs being a model of the data about those objects). This database model is designed principally for business users. Compare this to a physical model, which is all about the structure of the data. In this model, you’ll be dealing with tables, columns, relationships, and foreign keys, which distinguish the connections between the tables.

Now, let’s say you’re only focused on representing your data organization and architecture graphically, putting aside the physical usage or database management framework. In cases like these, a logical model could be the way to go. Examples of these types of databases include relational (dealing with data as tables or relations), network (putting data in the form of records), and hierarchical (which is a progressive tree-type structure, with each branch of the tree showing related records). These models all feature a high degree of standardization and cover all entities in the dataset and the relationships between them.

Got a wide array of different objects and types of data to deal with? Consider an object-oriented database model, sometimes called a “hybrid model.” These models look at their contained data as a collection of reusable software pieces, all with related features. They also consolidate tables but aren’t limited to the tables, giving you freedom when dealing with lots of varied data. You can use this kind of model for multimedia items you can’t put in a relational database or to create a hypertext database to connect to another object and sort out divergent information.

Lastly, we can’t help but mention the star schema here, which has elements arranged around a central core and looks like an asterisk. This model is great for querying informational indexes as part of a larger data pool. It’s used to dig up insights for business users, OLAP cubes, analytics apps, and ad-hoc analyses. It’s a simple, yet powerful, structure that sees a lot of usage, despite its simplicity.

Now What?

Whether you’re building awesome analytics into your app or empowering in-house users to get more out of your data, knowing what you’re doing with your data is key to maintaining the right models. Once you’ve picked your database, it’s time to pick your data model, with an eye towards what you want to do with it once it’s hooked into your BI solution.

Worried about losing customers? (Who isn’t?) A predictive churn model can help you get ahead of the curve by putting time and attention into relationships that are at risk of going sour. On the other side of the coin, predictive up- and cross-sell models can show you where you can get more money out of a customer and which ones are ripe to deepen your financial relationship.

What about your marketing efforts? A customer segmentation data model can help you understand the buying behaviors of your current customers and target groups and which marketing plays are having the desired effect. Or go beyond marketing with “next-best-action models” that take into account life events, purchasing behaviors, social media, and anything else you can get your hands on so that you can figure out what’s the next action with a given target (email, ads, phone call, etc.) to have the greatest impact. And predictive analyses aren’t just for humancentric activities—manufacturing and logistics companies can take advantage of maintenance models that can let you circumvent machine breakdowns based on historical data. Don’t get caught without a vital piece of equipment again.

Bringing It All Together with BI

Staying focused on your long-term goals is an important key to success. Whether you’re building a game-changing product or rebuilding your data model, having a firmly-defined goal makes all the difference when it comes to the success of your enterprise. If you’re already migrating your data to the cloud, then you’re at the perfect juncture to pick the right database and data models for your eventual use cases. Once these are set up, they’ll integrate seamlessly with your BI tool (and if you don’t have one yet, it’ll be the perfect time to implement one). Big moves like this represent big challenges, but also big opportunities to make lay the foundation for whatever you’re planning on building. Then you just have to build it!

Source: Cloud Migrations: Big Challenges, Big Opportunities by analyticsweek

The IBM “endorsement effect” has often shaped the computer industry over the years. In 1981, when IBM entered the personal computer business, the company decisively pushed an upstart technology into the mainstream.

In 2000, the open-source operating system Linux was viewed askance in many corporations as an oddball creation and even legally risky to use, since the open-source ethos prefers sharing ideas rather than owning them. But IBM endorsed Linux and poured money and people into accelerating the adoption of the open-source operating system.

On Monday, IBM is to announce a broadly similar move in big data software. The company is placing a large investment — contributing software developers, technology and education programs — behind an open-source project for real-time data analysis, called Apache Spark.

The commitment, according to Robert Picciano, senior vice president for IBM’s data analytics business, will amount to “hundreds of millions of dollars” a year.

In the big data software market, much of the attention and investment so far has been focused on Apache Hadoop and the companies distributing that open-source software, including Cloudera, Hortonworks and MapR. Hadoop, put simply, is the software that makes it possible to handle and analyze vast volumes of all kinds of data. The technology came out of the pure Internet companies like Google and Yahoo, and is increasingly being used by mainstream companies, which want to do similar big data analysis in their businesses.

But if Hadoop opens the door to probing vast volumes of data, Spark promises speed. Real-time processing is essential for many applications, from analyzing sensor data streaming from machines to sales transactions on online marketplaces. The Spark technology was developed at the Algorithms, Machines and People Lab at the University of California, Berkeley. A group from the Berkeley lab founded a company two years ago, Databricks, which offers Spark software as a cloud service.

Spark, Mr. Picciano said, is crucial technology that will make it possible to “really deliver on the promise of big data.” That promise, he said, is to quickly gain insights from data to save time and costs, and to spot opportunities in fields like sales and new product development.

IBM said it will put more than 3,500 of its developers and researchers to work on Spark-related projects. It will contribute machine-learning technology to the open-source project, and embed Spark in IBM’s data analysis and commerce software. IBM will also offer Spark as a service on its programming platform for cloud software development, Bluemix. The company will open a Spark technology center in San Francisco to pursue Spark-based innovations.

And IBM plans to partner with academic and private education organizations including UC Berkeley’s AMPLab, DataCamp, Galvanize and Big Data University to teach Spark to as many as 1 million data engineers and data scientists.

Ion Stoica, the chief executive of Databricks, who is a Berkeley computer scientist on leave from the university, called the IBM move “a great validation for Spark.” He had talked to IBM people in recent months and knew they planned to back Spark, but, he added, “the magnitude is impressive.”

With its Spark initiative, analysts said, IBM wants to lend a hand to an open-source project, woo developers and strengthen its position in the fast-evolving market for big data software.

By aligning itself with a popular open-source project, IBM, they said, hopes to attract more software engineers to use its big data software tools, too. “It’s first and foremost a play for the minds — and hearts — of developers,” said Dan Vesset, an analyst at IDC.

IBM is investing in its own future as much as it is contributing to Spark. IBM needs a technology ecosystem, where it is a player and has influence, even if it does not immediately profit from it. IBM mainly makes its living selling applications, often tailored to individual companies, which address challenges in their business like marketing, customer service, supply-chain management and developing new products and services.

“IBM makes its money higher up, building solutions for customers,” said Mike Gualtieri, a analyst for Forrester Research. “That’s ultimately why this makes sense for IBM.”

To read the original article on The New York Times, click here.

Source: IBM Invests to Help Open-Source Big Data Software — and Itself

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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