Tag Archives: Analytics
While CIOs are urged to rethink of backup strategies following warnings from leading analysts that companies are wasting billions on unnecessary storage, consultants and IT solution vendors are selling “Big Data” narratives to these CIOs as a storage optimization strategy.
What a CIO must do is ask:
Do you think a Backup Strategy is same as a Big Data strategy?
Is your MO – “I must invest in Big Data because my competitor is”?
Do you think Big Data and “data analysis” are synonyms?
Most companies invest very little in their storage technologies, while spending on server and network technologies primarily for backup. Further, the most common mistake businesses make is to fail to update their backup policies. It is not unusual for companies to be using backup policies that are years or even decades old, which do not discriminate between business-critical files and the personal music files of employees.
Web giants like Facebook and Yahoo generally aren’t dealing with Big Data. They run their own giant, in-house “clusters” – collections of powerful servers – for crunching data. But, it appears that those clusters are unnecessary for many of the tasks which they’re handed. In the case of Facebook, most of the jobs engineers ask their clusters to perform are in the “megabyte to gigabyte” range, which means they could easily be handled on a single computer – even a laptop.
The necessity of breaking problems into many small parts, and processing each on a large array of computers, characterizes classic Big Data problems like Google’s need to compute the rank of every single web page on the planet.
In, Nobody ever got fired for buying a cluster, Microsoft Research points out that a lot of the problems solved by engineers at even the most data-hungry firms don’t need to be run on clusters. Why is that a problem? It is because, there are vast classes of problems for which these clusters are relatively inefficient, or a very inappropriate, solution.
Here is an example of a post exhorting readers to “Incorporate Big Data Into Your Small Business” that is about a quantity of data that probably wouldn’t strain Google Docs, much less Excel on a single laptop. In other words, most businesses are in dealing with small data. It’s very important stuff but it has little connection to the big kind.
Let us lose the habit of putting “big” in front of data to make it sound important. After all, supersizing your data, just because you can, is going to cost you a lot more and may yield a lot less.
So what is it? Big Data, small Data, or Smart Data?
Gregor Mendel uncovered the secrets of genetic inheritance with just enough data to fill a notebook. The important thing is gathering the right data, not gathering some arbitrary quantity of it.
Data Science should change how your businesses are run
The importance of data science is becoming more and more clear. Marc Benioff says, “I think for every company, the revolution in data science will fundamentally change how we run our business”. “There’s just a huge amount more data than ever before, our greatest challenge is making sense of that data”. He goes on to say that “we need a new generation of executives who understand how to manage and lead through data. And we also need a new generation of employees who are able to help us organize and structure our business around data”. Mark then says “when I look at the next set of technologies that we have to build at Salesforce, it is all data science based technology.” Ram Charan in his article in Fortune Magazine “says to thrive, companies—and the execs who run them, must transform into math machines” (The Algorithmic CEO, Fortune Magazine, March 2015, page 45).
With such powerful endorsements for data science, the question you may be asking is when should you hire a data scientist or two. The answer has multiple answers. I liken data science to any business research. You need to do your upfront homework for the data scientists you hire to be effective.
Create a situation analysis before you start
You need to start by defining your problem—are you losing sales, finding it takes too long to manufacturer something, less profitable than you would like to be, and the list goes on. Next, you should create a situational analysis. You want to arm your data scientists with as much information as possible to define what you want them to solve or change. Make sure that you are as concrete as possible here. Data scientists struggle when the business people that they work with are vague. As well, it is important that you indicate what kinds of business changes will be considered if the model and data deliver this results or that result.
Next you need to catalog the data that you already have which is relevant to the business problem. Without relevant data there is little that the data scientist can do to help you. With relevant data sources in hand, you need to define the range of actions that you can possibly take once a model has been created.
Be realistic about what is required
With these things in hand, it may be time to hire some data scientists. As you start your process, you need to be realistic about the difficulty of getting a top flight data scientist. Many of my customers have complained about the difficulty competing with Google and other tech startups. As important, “there is a huge variance in the quality and ability of data scientists”. (Data Science for Business, Foster Provost, O’Reilly, page 321). Once you have hired someone, you need to keep in mind that effective data science requires business and data science collaboration. As well, please know that data scientist struggle when business people don’t appreciate the effort needed to get an appropriate training data set or model evaluation procedures.
Make sure internal or external data scientists give you an effective proposal
Once Once your data scientists are in place, you should realize that a data scientist worth their salt will create a proposal back to you. As we have said, it is important that you know what kinds of things will happen if the model and data delivery this results or that result. Data scientist in turn will be able to narrow things down to a dollar impact.
Their proposal should start by sharing their understanding of the business and the data which is available. What business problems are they trying to solve? Next the data scientist may define things like whether supervised or unsupervised learning will be used. Next they should openly discuss what efforts will be involved in data preparation. They should tell you here about the values for the target variable (whose values will be predicted). They should describe next their modeling approach and whether more than one model is be evaluated and then how models will be compared and final model be selected. And finally, they should discuss how the model will be evaluated and deployed. Are there evaluation and setup metrics? Data scientists can dedicate time and resources in their proposal to determining what things are real versus expected drivers.
To make all this work, it can be a good idea for data scientist to talk in their proposal about likelihood because business people that have not been through a quantitative MBA do not understand or remember statistics. It is important as well that data scientist before they begin ask business people the so what questions if the situation analysis is inadequate.
Leading an internal analytics team
In some cases, analytical teams will be built internally. Where this occurs, it is really importantly that the analytic leader have good people skills. They need as well to be able to set expectations that people will be making decisions from data and analysis. This includes having the ability to push back when someone comes to them will a recommendation based on gut feel.
The leader needs to hire smart analysts. To keep them, they need a stimulating and supportive work environment. Tom Davenport says analysts are motivated by interesting and challenging work that allows them to utilize their highly specialize skills. Like millenials, money is nice for analysts but they are more motivated more by exciting work and having the opportunity to grow and stretch their skills. Please know that data scientists want to spend time refining analytical models rather than doing simple analyses and report generation. Most importantly they want to do important work that makes a meaningful contribution. To do this, they want to feel supported and valued but have autonomy at work. This includes the freedom to organize their work. At the same time, analysts like to work together. And they like to be surrounded by other smart and capable collogues. Make sure to treat your data scientists as a strategic resource. This means you need development plans, career plans, and performance management processes.
As we have discussed, make sure to do your homework before contracting or hiring for data scientists. Once you have done your homework, if you are an analytic leader, make sure that you create a stimulating environment. Additionally, prove the value of analytics by signing up for results that demonstrate data modeling efficacy. To do this, look here for business problems that will lead to a big difference. And finally if you need an analytics leader to emulate, look no further than Brian Cornell, the new CEO of Target.
Myles in Twitter: @MylesSuer
For those hoping to push through a hard-hitting analytics effort that will serve as a beacon of light within an otherwise calcified organization, there’s probably a lot of work cut out for you. Evolving into an organization that fully grasps the power and opportunities of data analytics requires cultural change, and this is a challenge organizations have only begin to grasp.
“Sitting down with pizza and coffee could get you around can get around most of the technical challenges,” explained Sam Ransbotham, Ph.D, associate professor Boston College, at a recent panel webcast hosted by MIT Sloan Management Review, “but the cultural problems are much larger.”
That’s one of the key takeaways from a the panel, in which Ransbotham was joined by Tuck Rickards, head of digital transformation practice at Russell Reynolds Associates, a digital recruiting firm, and Denis Arnaud, senior data scientist Amadeus Travel Intelligence. The panel, which examined the impact of corporate culture on data analytics, was led by Michael Fitzgerald, contributing editor at MIT Sloan Management Review.
The path to becoming an analytics-driven company is a journey that requires transformation across most or all departments, the panelists agreed. “It’s fundamentally different to be a data-driven decision company than kind of a gut-feel decision-making company,” said Rickards. “Acquiring this capability to do things differently usually requires a massive culture shift.”
That’s because the cultural aspects of the organization – “the values, the behaviors, the decision making norms and the outcomes go hand in hand with data analytics,” said Ransbotham. “It doesn’t do any good to have a whole bunch of data processes if your company doesn’t have the culture to act on them and do something with them.” Rickards adds that bringing this all together requires an agile, open source mindset, with frequent, open communication across the organization.
So how does one go about building and promoting a culture that is conducive to getting the maximum benefit from data analytics? The most important piece is being about people who ate aware and skilled in analytics – both from within the enterprise and from outside, the panelists urged. Ransbotham points out that it may seem daunting, but it’s not. “This is not some gee-whizz thing,” he said. “We have to get rid of this mindset that these things are impossible. Everybody who has figured it out has figured it out somehow. We’re a lot more able to pick up on these things that we think — the technology is getting easier, it doesn’t require quite as much as it used to.”
The key to evolving corporate culture to becoming more analytics-driven is to identify or recruit enlightened and skilled individuals who can provide the vision and build a collaborative environment. “The most challenging part is looking for someone who can see the business more broadly, and can interface with the various business functions –ideally, someone who can manage change and transformation throughout the organization,” Rickards said.
Arnaud described how his organization – an online travel service — went about building an espirit de corps between data analytics staff and business staff to ensure the success of their company’s analytics efforts. “Every month all the teams would do a hands-on workshop, together in some place in Europe [Amadeus is headquartered in Madrid, Spain].” For example, a workshop may focus on a market analysis for a specific customer, and the participants would explore the entire end-to-end process for working with the customer, “from the data collection all the way through to data acquisition through data crunching and so on. The one knowing the data analysis techniques would explain them, and the one knowing the business would explain that, and so on.” As a result of these monthly workshops, business and analytics teams members have found it “much easier to collaborate,” he added.
Web-oriented companies such as Amadeus – or Amazon and eBay for that matter — may be paving the way with analytics-driven operations, but companies in most other industries are not at this stage yet, both Rickards and Ransbotham point out. The more advanced web companies have built “an end-to-end supply chain, wrapped around customer interaction,” said Rickards. “If you think of most traditional businesses, financial services or automotive or healthcare are a million miles away from that. It starts with having analytic capabilities, but it’s a real journey to take that capability across the company.”
The analytics-driven business of the near future – regardless of industry – will likely to be staffed with roles not seen as of yet today. “If you are looking to re-architect the business, you may be imagining roles that you don’t have in the company today,” said Rickards. Along with the need for chief analytics officers, data scientists, and data analysts, there will be many new roles created. “If you are on the analytics side of this, you can be in an analytics group or a marketing group, with more of a CRM or customer insights title. Yu can be in a planning or business functions. In a similar way on the technology side, there are people very focused on architecture and security.”
Ultimately, the demand will be for leaders and professionals who understand both the business and technology sides of the opportunity, Rickards continued. Ultimately, he added, “you can have good people building a platform, and you can have good data scientists. But you better have someone on the top of that organization knowing the business purpose.’
I recently got to talk to several senior IT leaders about their views on information governance and analytics. Participating were a telecom company, a government transportation entity, a consulting company, and a major retailer. Each shared openly in what was a free flow of ideas.
The CEO and Corporate Culture is critical to driving a fact based culture
I started this discussion by sharing the COBIT Information Life Cycle. Everyone agreed that the starting point for information governance needs to be business strategy and business processes. However, this caused an extremely interesting discussion about enterprise analytics readiness. Most said that they are in the midst of leading the proverbial horse to water—in this case the horse is the business. The CIO in the group said that he personally is all about the data and making factual decisions. But his business is not really there yet. I asked everyone at this point about the importance of culture and the CEO. Everyone agreed that the CEO is incredibly important in driving a fact based culture. Apparent, people like the new CEO of Target are in the vanguard and not the mainstream yet.
KPIs need to be business drivers
The above CIO said that too many of his managers are operationally, day-to-day focused and don’t understand the value of analytics or of predictive analytics. This CIO said that he needs to teach the business to think analytically and to understand how analytics can help drive the business as well as how to use Key Performance Indicators (KPIs). The enterprise architect in the group shared at this point that he had previously worked for a major healthcare organization. When organization was asked to determine a list of KPIs, they came back 168 KPIs. Obviously, this could not work so he explained to the business that an effective KPI must be a “driver of performance”. He stressed to the healthcare organization’s leadership the importance of having less KPIs and of having those that get produced being around business capabilities and performance drivers.
IT needs increasingly to understand their customers business models
I shared at this point that I visited a major Italian bank a few years ago. The key leadership had high definition displays that would roll by an analytic every five minutes. Everyone laughed at the absurdity of having so many KPIs. But with this said, everyone felt that they needed to get business buy in because only the business can derive the value from acting upon the data. According to this group of IT leaders, this causing them more and more to understand their customer’s business models.
Others said that they were trying to create an omni-channel view of customers. The retailer wanted to get more predictive. While Theodore Levitt said the job of marketing is to create and keep a customer. This retailer is focused on keeping and bringing back more often the customer. They want to give customers offers that use customer data that to increase sales. Much like what I described recently was happening at 58.com, eBay, and Facebook.
Most say they have limited governance maturity
We talked about where people are in their governance maturity. Even though, I wanted to gloss over this topic, the group wanted to spend time here and compare notes between each other. Most said that they were at stage 2 or 3 in in a five stage governance maturity process. One CIO said, gee does anyone ever at level 5. Like analytics, governance was being pushed forward by IT rather than the business. Nevertheless, everyone said that they are working to get data stewards defined for each business function. At this point, I asked about the elements that COBIT 5 suggests go into good governance. I shared that it should include the following four elements: 1) clear information ownership; 2) timely, correct information; 3) clear enterprise architecture and efficiency; and 4) compliance and security. Everyone felt the definition was fine but wanted specifics with each element. I referred them and you to my recent article in COBIT Focus.
CIO says they are the custodians of data only
At this point, one of the CIOs said something incredibly insightful. We are not data stewards. This has to be done by the business—IT is the custodians of the data. More specifically, we should not manage data but we should make sure what the business needs done gets done with data. Everyone agreed with this point and even reused the term, data custodians several times during the next few minutes. Debbie Lew of COBIT said just last week the same thing. According to her, “IT does not own the data. They facilitate the data”. From here, the discussion moved to security and data privacy. The retailer in the group was extremely concerned about privacy and felt that they needed masking and other data level technologies to ensure a breach minimally impacts their customers. At this point, another IT leader in the group said that it is the job of IT leadership to make sure the business does the right things in security and compliance. I shared here that one my CIO friends had said that “the CIOs at the retailers with breaches weren’t stupid—it is just hard to sell the business impact”. The CIO in the group said, we need to do risk assessments—also a big thing for COBIT 5–that get the business to say we have to invest to protect. “It is IT’s job to adequately explain the business risk”.
Is mobility a driver of better governance and analytics?
Several shared towards the end of the evening that mobility is an increasing impetus for better information governance and analytics. Mobility is driving business users and business customers to demand better information and thereby, better governance of information. Many said that a starting point for providing better information is data mastering. These attendees felt as well that data governance involves helping the business determine its relevant business capabilities and business processes. It seems that these should come naturally, but once again, IT for these organizations seems to be pushing the business across the finish line.
Blogs and Articles:
I recently got to meet with a very enlightened insurance company which was actively turning their SWOTT analysis (with the second T being trends) into concrete action. They shared with me that they view their go forward “right to win” being determined by the quality of customer experience they deliver to customers through their traditional channels and increasingly through “digital channels”. One marketing leader joked early on that “it’s no longer about the money; it is about the experience”. The marketing and business leaders that I met with made it extremely clear that they have a sense of urgency to respond to what it saw as significant market changes on the horizon. What this company wanted to achieve was a single view of customer across each of its distribution channel as well as their agent population. Typical of many businesses today, they had determined that they needed an automated, holistic view into things like its customer history. Smartly, this business wanted to put together its existing customer data with its customer leads.
Using Data to Accelerate the Percentage Customers that are Cross Sold
Taking this step was seen as allowing them to understand when an existing customer is also a lead for another product. With this knowledge, they wanted to provide them with special offers to accelerate their conversion from lead to being a customer with more than one product. What they wanted to do here reminded me of the capabilities of 58.com, eBay, and other Internet pure plays. The reason for doing this well was described recently by Gartner. Gartner suggests that increasing business success is determined by what they call “business moments”. Without a first rate experience that builds upon what this insurance company already knows about its customers, this insurance company worries it could be increasing at risk by Internet pure plays. As important, like many businesses, the degree of cross sell is for many businesses a major determinant of whether a customer is profitable or not.
Getting Customer Data Right is Key to Developing a Winning Digital Experience
To drive a first rate digital experience, this insurance company wanted to apply advanced analytics to a single view of customer and prospect data. This would allow them to do things like conduct nearest neighbor predictive analysis and modeling. In this form of analysis, “the goal is to predict whether a new customer will respond to an offer based on how other similar customers have responded” (Data Science for Business, Foster Provost, O’Reilly, 2013, page 147).
What has limiting this business like so many others is that their customer data is scattered across many enterprise systems. For just for one division, they have more than one Salesforce instance. Yet this company’s marketing team knew to keep its customers, it needed to be able to service them omnichannel and establish a single unified customer experience. To make this happen, they needed to for the first to share holistic customer information across their ecosystems. At the same time, they knew that they would needed to protect their customer’s privacy—i.e. only certain people would be able to see certain information. They wanted by role that the ability to selective mask data and protect their customer in particular consumers by only allowing certain users in defense parlance, with a need to know, to see a subset of the holistic set of information collected. When asked about the need for a single view of customer, the digital marketing folks openly shared that they perceived the potential for external digital market entrants—ala Porter’s five forces of competition. This firm saw them either as taking market share from them or effectively disintermediating them over time them from their customers as more and more customers move their insurance purchasing of Insurance to the Web. Given the risk, their competitive advantage needed to move to knowing better their customer and being able to respond better to them on the web. This clearly included new customers that are trying to win in the language of Theodore Levitt.
Competing on Customer Experience
In sum, this insurance company smartly felt that they needed to compete on customer experience to pull out a new phrase for me and this required superior knowledge of existing and new customers. This means they needed as complete and correct view of customers as possible including addresses, connection preferences, and increasingly social media responses. This means competitively responding directly to those that have honed their skills in web design, social presence, and advanced analytics. To do this, they will create predictive capabilities that will make use of their superior customer data. Clearly, without this prescience of thinking, this moment will not be like the strategic collision of Starbucks and Fast Food Vendors where the desire to grow forced competition between the existing player and new entrants wanting to claim a portion of the existing market player’s business.
Blogs and Articles
Who remembers their first game of Pong? Celebrating more than 40 years of innovation, gaming is no longer limited to monochromatic screens and dedicated, proprietary platforms. The PC gaming industry is expected to exceed $35bn by 2018. Phone and handheld games is estimated at $34bn in 5 years and quickly closing the gap. According to EEDAR, 2014 recorded more than 141 million mobile gamers just in North America, generating $4.6B in revenue for mobile game vendors.
This growth has spawned a growing list of conferences specifically targeting gamers, game developers, the gaming industry and more recently gaming analytics! This past weekend in Boston, for example, was PAX East where people of all ages and walks of life played games on consoles, PC, handhelds, and good old fashioned board games. With my own children in attendance, the debate of commercial games versus indie favorites, such as Minecraft , dominates the dinner table.
Online games are where people congregate online, collaborate, and generate petabytes of data daily. With the added bonus of geospatial data from smart phones, the opportunity for more advanced analytics. Some of the basic metrics that determine whether a game is successful, according to Ninja Metrics, include:
- New Users, Daily Active Users, Retention
- Revenue per user
- Session length and number of sessions per user
Additionally, they provide predictive analytics, customer lifetime value, and cohort analysis. If this is your gig, there’s a conference for that as well – the Gaming Analytics Summit !
At the Game Developers Conference recently held in San Francisco, the focus of this event has shifted over the years from computer games to new gaming platforms that need to incorporate mobile, smartphone, and online components. In order to produce a successful game, it requires the following:
- Needs to be able to connect to a variety of devices and platforms
- Needs to use data to drive decisions and improve user experience
- Needs to ensure privacy laws are adhered to.
Developers are able to quickly access online gaming data and tweak or change their sprites’ attributes dynamically to maximize player experience.
When you look at what is happening in the gaming industry, you can start to see why colleges and universities like my own alma mater, WPI, now offers a computer science degree in Interactive Media and Game Design degree . The IMGD curriculum includes heavy coursework in data science, game theory, artificial intelligence and story boarding. When I asked a WPI IMGD student about what they are working on, they are mapping out decision trees that dictate what adversary to pop up based on the player’s history (sounds a lot like what we do in digital marketing…).
As we start to look at the Millennial Generation entering into the workforce, maybe we should look at our own recruiting efforts and consider game designers. They are masters in analytics and creativity with an appreciation for the importance of great data. Combining the magic and the math makes a great gaming experience. Who wouldn’t want that for their customers?
On March 25th, Josh Lee, Global Director for Insurance Marketing at Informatica and Cindy Maike, General Manager, Insurance at Hortonworks, will be joining the Insurance Journal in a webinar on “How to Become an Analytics Ready Insurer”.
Register for the Webinar on March 25th at 10am Pacific/ 1pm Eastern
Josh and Cindy exchange perspectives on what “analytics ready” really means for insurers, and today we are sharing some of our views (join the webinar to learn more). Josh and Cindy offer perspectives on the five questions posed here. Please join Insurance Journal, Informatica and Hortonworks on March 25th for more on this exciting topic.
See the Hortonworks site for a second posting of this blog and more details on exciting innovations in Big Data.
- What makes a big data environment attractive to an insurer?
CM: Many insurance companies are using new types of data to create innovative products that better meet their customers’ risk needs. For example, we are seeing insurance for “shared vehicles” and new products for prevention services. Much of this innovation is made possible by the rapid growth in sensor and machine data, which the industry incorporates into predictive analytics for risk assessment and claims management.
Customers who buy personal lines of insurance also expect the same type of personalized service and offers they receive from retailers and telecommunication companies. They expect carriers to have a single view of their business that permeates customer experience, claims handling, pricing and product development. Big data in Hadoop makes that single view possible.
JL: Let’s face it, insurance is all about analytics. Better analytics leads to better pricing, reduced risk and better customer service. But here’s the issue. Existing data sources are costly in storing vast amounts of data and inflexible to adapt to changing needs of innovative analytics. Imagine kicking off a simulation or modeling routine one evening only to return in the morning and find it incomplete or lacking data that requires a special request of IT.
This is where big data environments are helping insurers. Larger, more flexible data sets allowing longer series of analytics to be run, generating better results. And imagine doing all that at a fraction of the cost and time of traditional data structures. Oh, and heaven forbid you ask a mainframe to do any of this.
- So we hear a lot about Big Data being great for unstructured data. What about traditional data types that have been used in insurance forever?
CM: Traditional data types are very important to the industry – it drives our regulatory reporting and much of the performance management reporting. This data will continue to play a very important role in the insurance industry and for companies.
However, big data can now enrich that traditional data with new data sources for new insights. In areas such as customer service and product personalization, it can make the difference between cross-selling the right products to meet customer needs and losing the business. For commercial and group carriers, the new data provides the ability to better analyze risk needs, price accordingly and enable superior service in a highly competitive market.
JL: Traditional data will always be around. I doubt that I will outlive a mainframe installation at an insurer; which makes me a little sad. And for many rote tasks like financial reporting, a sales report, or a commission statement, those are sufficient. However, the business of insurance is changing in leaps and bounds. Innovators in data science are interested in correlating those traditional sources to other creative data to find new products, or areas to reduce risk. There is just a lot of data that is either ignored or locked in obscure systems that needs to be brought into the light. This data could be structured or unstructured, it doesn’t matter, and Big Data can assist there.
- How does this fit into an overall data management function?
JL: At the end of the day, a Hadoop cluster is another source of data for an insurer. More flexible, more cost effective and higher speed; but yet another data source for an insurer. So that’s one more on top of relational, cubes, content repositories, mainframes and whatever else insurers have latched onto over the years. So if it wasn’t completely obvious before, it should be now. Data needs to be managed. As data moves around the organization for consumption, it is shaped, cleaned, copied and we hope there is governance in place. And the Big Data installation is not exempt from any of these routines. In fact, one could argue that it is more critical to leverage good data management practices with Big Data not only to optimize the environment but also to eventually replace traditional data structures that just aren’t working.
CM: Insurance companies are blending new and old data and looking for the best ways to leverage “all data”. We are witnessing the development of a new generation of advanced analytical applications to take advantage of the volume, velocity, and variety in big data. We can also enhance current predictive models, enriching them with the unstructured information in claim and underwriting notes or diaries along with other external data.
There will be challenges. Insurance companies will still need to make important decisions on how to incorporate the new data into existing data governance and data management processes. The Chief Data or Chief Analytics officer will need to drive this business change in close partnership with IT.
- Tell me a little bit about how Informatica and Hortonworks are working together on this?
JL: For years Informatica has been helping our clients to realize the value in their data and analytics. And while enjoying great success in partnership with our clients, unlocking the full value of data requires new structures, new storage and something that doesn’t break the bank for our clients. So Informatica and Hortonworks are on a continuing journey to show that value in analytics comes with strong relationships between the Hadoop distribution and innovative market leading data management technology. As the relationship between Informatica and Hortonworks deepens, expect to see even more vertically relevant solutions and documented ROI for the Informatica/Hortonworks solution stack.
CM: Informatica and Hortonworks optimize the entire big data supply chain on Hadoop, turning data into actionable information to drive business value. By incorporating data management services into the data lake, companies can store and process massive amounts of data across a wide variety of channels including social media, clickstream data, server logs, customer transactions and interactions, videos, and sensor data from equipment in the field.
Matching data from internal sources (e.g. very granular data about customers) with external data (e.g. weather data or driving patterns in specific geographic areas) can unlock new revenue streams.
See this video for a discussion on unlocking those new revenue streams. Sanjay Krishnamurthi, Informatica CTO, and Shaun Connolly, Hortonworks VP of Corporate Strategy, share their perspectives.
- Do you have any additional comments on the future of data in this brave new world?
CM: My perspective is that, over time, we will drop the reference to “big” or ”small” data and get back to referring simply to “Data”. The term big data has been useful to describe the growing awareness on how the new data types can help insurance companies grow.
We can no longer use “traditional” methods to gain insights from data. Insurers need a modern data architecture to store, process and analyze data—transforming it into insight.
We will see an increase in new market entrants in the insurance industry, and existing insurance companies will improve their products and services based upon the insights they have gained from their data, regardless of whether that was “big” or “small” data.
JL: I’m sure that even now there is someone locked in their mother’s basement playing video games and trying to come up with the next data storage wave. So we have that to look forward to, and I’m sure it will be cool. But, if we are honest with ourselves, we’ll admit that we really don’t know what to do with half the data that we have. So while data storage structures are critical, the future holds even greater promise for new models, better analytical tools and applications that can make sense of all of this and point insurers in new directions. The trend that won’t change anytime soon is the ongoing need for good quality data, data ready at a moment’s notice, safe and secure and governed in a way that insurers can trust what those cool analytics show them.
Please join us for an interactive discussion on March 25th at 10am Pacific Time/ 1pm Eastern Time.
Register for the Webinar on March 25th at 10am Pacific/ 1pm Eastern
In the 2011 film Moneyball Billy Beane introduced to the sports industry how to use data analytics to acquire statistically optimal players for the Oakland A’s. In the last 4 years, advancements in data collection, preparation, aggregation and advanced analytics technology have made it possible to broaden the scope of applying analytics beyond the game and player, drastically change the shape of an industry that has a long history built on tradition.
Last week, MIT Sloan held its 9th annual Sports Analytics Conference in Boston, MA. Amidst the 6 foot snow banks, sports fanatics and data scientists came together at this sold out event to discuss the increasing role of analytics in the sports industry. This year’s conference agenda included topics spanning game statistics and modeling, player contract and salary negotiations, dynamic ticket pricing, referee calls to improving fan experiences.
This latter topic, improving fan experiences, is one that has seen a boost in technology innovation such that data is more readily available for use in analytics. For example, newer NFL stadiums are wifi connected throughout so that fans can watch replays on their devices, tweet, and share selfies during the game. With mobile devices connected to the stadium’s wifi, franchises can drive revenue generating marketing campaigns to their home fan base throughout the game.
More important, however, is the need to keep the Millennial Generation interested in watching games live. In an article posted by TechRepublic, college students are more likely to leave a game during halftime if they are not able to connect to the internet or use social media. Teams need to keep fans in the stadiums so the goal needs to ensure the fan experience in a live venue matches what they can experience at home.
Innovation in advanced analytics and Big Data platforms such as Hadoop gives sports analysts the ability to access significant volumes of detailed data resulting in greater modeling accuracy. Streamlined data preparation tools speed the process from receiving raw data to delivering insight. Advanced analytics offered in the cloud as a service offers team owners and managers access to predictive analytics tools without having to manage and staff large data centers. Better visualization applications provide an effective way to communicate what the data means to those without a math degree.
When applying these innovations to new data sources while combining with advancements of analytics in sports, the results will be game changing far beyond what Billy Beane was able to accomplish with the Okland A’s.
Our congratulations to the winners of the top research papers submitted at the MIT Sloan Sports Analytics conference: Who Is Responsible For A Called Strike? and Counterpoints: Advanced Defensive Metrics for NBA Basketball. It will be interesting to see how these models will make an impact, with Spring Training and March Madness just around the corner. Maybe next year, we will see a submission on the dependencies of atmospheric conditions on football pressure and its impact on the NFL playoffs (PV=NRT) and get a data-driven explanation of Deflate Gate.
What does it take to be an analytics-driven business? That’s a question that requires a long answer. Recently, Gartner research director Lisa Kart took on this question, noting how the key to becoming an analytics-driven business.
So, the secret of becoming an analytics-driven business is to bust down the silos — easier than done, of course. The good news, as Kart tells it, is that one doesn’t need to be casting a wide net across the world in search of the right data for the right occasion. The biggest opportunities are with connecting the data you already have, she says.
Taking Kart’s differentiation of just-using-analytics versus analytics-driven culture a step further, hare is a brief rundown of how businesses just using analytics approach the challenge, versus their more enlightened counterparts:
Business just using analytics: Lots of data, but no one really understands how much is around, or what to do with it.
Analytics-driven business: The enterprise has a vision and strategy, supported from the top down, closely tied to the business strategy. Management also recognizes that existing data has great value to the business.
Business just using analytics: Every department does its own thing, with varying degrees of success.
Analytics-driven business: Makes connections between all the data – of all types — floating around the organization. For example, gets a cross-channel view of a customer by digging deeper and connecting the silos together to transform the data into something consumable.
Business just using analytics: Some people in marketing have been collecting customer data and making recommendations to their managers.
Analytics-driven business: Marketing departments, through analytics, engage and interact with customers, Kart says. An example would be creating high end, in-store customer experiences that gave customers greater intimacy and interaction.
Business just using analytics: The CFO’s staff crunches numbers within their BI tools and arrive at what-if scenarios.
Analytics-driven business: Operations and finance departments share online data to improve performance using analytics. For example, a company may tap into a variety of data, including satellite images, weather patterns, and other factors that may shape business conditions, Kart says.
Business just using analytics: Some quants in the organization pour over the data and crank out reports.
Analytics-driven business: Encourages maximum opportunities for innovation by putting analytics in the hands of all employees. Analytics-driven businesses recognize that more innovation comes from front-line decision-makers than the executive suite.
Business just using analytics: Decision makers put in report requests to IT for analysis.
Analytics-driven business: Decision makers can go to an online interface that enables them to build and display reports with a click (or two).
Business just using analytics: Analytics spits out standard bar charts, perhaps a scattergram.
Analytics-driven business: Decision makers can quickly visualize insights through 3D graphics, also reflecting real-time shifts.
January 21, 2009. Why in the world would that be a date to recall? Well, for one, it was the day after Barack Obama was inaugurated as the 44th President of the United States. And secondly, it was the day President Obama released an arguably game changing document, his Memorandum on Transparency and Open Government. This one document set the stage for a new era in how government would look at the data it collects and creates. Since that time, the world of data has changed dramatically! Consider this – new analytics tools, new data types, new devices creating data, new storage ideas, new visualization applications, new concepts, new laws – the list of innovations goes on.
But, all these great innovations are not really why I’m writing today. Today, I’d like to call your attention to a news article I read in NextGov, “Amid Open Data Push, Agencies Feel Urge for Analytics”. I have to admit, as I read this article, I found myself getting just a little bit giddy. Why? Great question, thanks for asking. J Before going on with my thoughts, please take a moment to read the article. Go ahead, I have time. I’ll wait.
Picking up where I left off…
Since 2009, the notion of “open data” has been discussed primarily from one of two main perspectives:
- Transparency of government to citizens – Accountability
- What the private sector can do – Innovation
No doubt, there have been significant advances on both of these topics. Yet, as important as these concepts are, budget and resource constraints can cause open data efforts to be prioritized lower than, say, a mission-critical program.
Of course, I get this – mission first – but, a couple years ago it hit me, maybe government agencies are not seeing a potential opportunity that’s sitting right in front of them. Along with the mandate to publish open data, is the opportunity to consume open data and get it into their analytics engines, thus, supporting the agency’s mission! Just this slight mind shift has the potential to turn open data initiatives into a means to create value. Now do you see why I am excited by the article? (If not, I’ll assume you’ve yet to read it.) I’m thrilled to see agencies adding a third perspective to the open data conversation:
- Consumption of open data – Improving an agency’s ability to deliver on its mission(s)
I am looking forward to following the success of any agency effort to take advantage of open data as a strategic resource. If you have other examples beyond the cases noted in the NextGov article, please share!