Category Archives: CIO
A Data Lake is a simple concept. They are a catchment area for data entering the organization. In the past, most businesses didn’t need to organize such a data store because almost all data was internal. It traveled via traditional ETL mechanisms from transactional systems to a data warehouse and then was sprayed around the business, as required.
When a good deal of data comes from external sources, or even from internal sources like log files, which never previously made it into the data warehouse, there is a need for an “operational data store.” This has definitely become the premier application for Hadoop and it makes perfect sense to me that such technology be used for a data catchment area. The neat thing about Hadoop for this application is that:
- It scales out “as far as the eye can see,” so there’s no likelihood of it being unable to manage the data volumes even when they grow beyond the petabyte level.
- It is a key-value store, which means that you don’t need to expend much effort in modeling data when you decide to accommodate a new data source. You just define a key and define the metadata at leisure.
- The cost of the software and the storage is very low.
So let’s imagine that we have a need for a data catchment area, because we have decided to collect data from log-files, mobile devices, social networks, from public data sources, or whatever. So let us also imagine that we have implemented Hadoop and some of its useful components and we have begun to collect data.
Is it reasonable to describe this as a data lake?
A Hadoop implementation should not be a set of servers randomly placed at the confluence of various data flows. The placement needs to be carefully considered and if the implementation is to resemble a “data lake” in any way, then it must be a well-engineered man-made lake. Since the data doesn’t just sit there until it evaporates but eventually flows to various applications, we should think of this as a “data reservoir” rather than a “data lake.”
There is no point in arranging all that data neatly along the aisles because when we get it, we may not know what we want to do with it at the time we get it. We should organize the data when we know that.
Another reason we should think of this as more like a reservoir than a lake is that we might like to purify the data a little before sending it down the pipes to applications or users that want to use it.
The start of the year is a great time to refresh and take a new look at your capabilities, goals, and plans for your future-state architecture. That being said, you have to take into consideration that the most scarce resource in your architecture is probably your own personal time.
Looking forward, here are three things that I would recommend that every architect do. I realize that all three of these relate to data, but as I have said in the eBook, Think “Data First” to Drive Business Value, we believe that data is the key bottleneck in your enterprise architecture in terms of slowing the delivery of business initiatives in support of your organization’s business strategy.
So, here are the recommendations. None of these will cost you anything if you are a current Informatica PowerCenter customer. And #2 and #3 are free regardless. It is only a matter of your time:
1. Take a look at the current Informatica Cloud offering and in particular the templating capabilities.
Informatica Cloud is probably much more capable than you think. The standard templating functionality supports very complex use cases and does it all from a very easy to use, no-coding, user interface. It comes with a strong library of integration stubs that can be dragged & dropped into Microsoft Viseo to create complex integrations. Once the flow is designed in Viseo, it can be easily imported into Informatica Cloud and from there users have a Wizard-driven UI to do the final customization for sources, targets, mappings, transformations, filters, etc. It is all very powerful and easy to use.
- YouTube: Building Custom templates https://www.youtube.com/watch?v=yHmFkxov6bs
- 30 day free Informatica Cloud trial. http://more.informatica.com/en/cloud_trial/org?offer=30day-ICwebPage
Why This Matters to Architects
- You will see how easy it is for new groups to get going with fairly complex integrations.
- This is a great tool for departmental or new user use, and it will be completely compatible with the rest of your Informatica architecture – not another technology silo for you to manage.
- Any mapping created for Informatica on-premise can also run on the cloud version.
2. Download Informatica Rev and understand what it can do for your analysts and “data wranglers.”
Your data analysts are spending 80% of their time managing their data and only 20% on the actual analysis they are trying to provide. Informatica Rev is a great way to prepare your data before use in analytics tools such as Qlik, Tableau, and others.
With Informatica Rev, people who are not data experts can access, mashup, prototype and cleanse their data all in a User Interface that looks like a spreadsheet and requires no previous experience in data tools.
- For a free Informatica Rev download https://rev.informatica.com/
- Informatica Rev (Project Springbok) demo https://www.youtube.com/watch?v=0F_58bHKDDs
Why This Matters for Architects
- Your data analysts are going to use analytics tools with or without the help of IT. This enables you to help them while ensuring that they are managing their data well and optimizing their productivity.
- This tool will also enable them to share their “data recipes” and for IT to be involved in how they access and use the organization’s data.
3. Look at the new features in PowerCenter 9.6. First, upgrade to 9.6 if you haven’t already, and particularly take a good look at these new capabilities that are bundled in every version. Many people we talk to have 9.6 but don’t realize the power of what they already own.
- Profiling: Discover and analyze your data quickly. Find relationships and data issues.
- Data Services: This presents any JDBC or ODBC repository as a logical data object. From there you can rapidly prototype new applications using these logical objects without worrying about the complexities of the underlying repositories. It can also do data cleansing on the fly.
- Webinar: Great Data by Design. https://www.brighttalk.com/webcast/10477/104939
- PowerCenter 9.6 deep dive demo https://www.brighttalk.com/webcast/10477/110535
Why This Matters for Architects
- The key challenge for IT and for Architects is to be able to deliver at the “speed of business.” These tools can dramatically improve the productivity of your team and speed the delivery of projects for your business “customers.”
Taking the time to understand what these tools can do in terms of increasing the productivity of your IT team and enabling your end users to self-service will make you a better business partner overall and increase your influence across the organization. Have a great year!
The thing that resonates today, in the odd context of big data, is that we may all need to look in the mirror, hold a thumb drive full of information in our hands, and concede once and for all It’s not the data… it’s us.
Many organizations have a hard time making something useful from the ever-expanding universe of big-data, but the problem doesn’t lie with the data: It’s a people problem.
The contention is that big-data is falling short of the hype because people are:
- too unwilling to create cultures that value standardized, efficient, and repeatable information, and
- too complex to be reduced to “thin data” created from digital traces.
Evan Stubbs describes poor data quality as the data analyst’s single greatest problem.
About the only satisfying thing about having bad data is the schadenfreude that goes along with it. There’s cold solace in knowing that regardless of how poor your data is, everyone else’s is equally as bad. The thing is poor quality data doesn’t just appear from the ether. It’s created. Leave the dirty dishes for long enough and you’ll end up with cockroaches and cholera. Ignore data quality and eventually you’ll have black holes of untrustworthy information. Here’s the hard truth: we’re the reason bad data exists.
I will tell you that most data teams make “large efforts” to scrub their data. Those “infrequent” big cleanups however only treat the symptom, not the cause – and ultimately lead to inefficiency, cost, and even more frustration.
It’s intuitive and natural to think that data quality is a technological problem. It’s not; it’s a cultural problem. The real answer is that you need to create a culture that values standardized, efficient, and repeatable information.
If you do that, then you’ll be able to create data that is re-usable, efficient, and high quality. Rather than trying to manage a shanty of half-baked source tables, effective teams put the effort into designing, maintaining, and documenting their data. Instead of being a one-off activity, it becomes part of business as usual, something that’s simply part of daily life.
However, even if that data is the best it can possibly be, is it even capable of delivering on the big-data promise of greater insights about things like the habits, needs, and desires of customers?
Despite the enormous growth of data and the success of a few companies like Amazon and Netflix, “the reality is that deeper insights for most organizations remain elusive,” write Mikkel Rasmussen and Christian Madsbjerg in a Bloomberg Businessweek blog post that argues “big-data gets people wrong.”
Big-data delivers thin data. In the social sciences, we distinguish between two types of human behavior data. The first – thin data – is from digital traces: He wears a size 8, has blue eyes, and drinks pinot noir. The second – rich data – delivers an understanding of how people actually experience the world: He could smell the grass after the rain, he looked at her in that special way, and the new running shoes made him look faster. Big-data focuses solely on correlation, paying no attention to causality. What good is thin “information” when there is no insight into what your consumers actually think and feel?
Accenture reported only 20 percent of the companies it profiled had found a proven causal link between “what they measure and the outcomes they are intending to drive.”
Now, I can contend they keys to transforming big-data to strategic value are critical thinking skills.
Where do we get such skills? People, it seems, are both the problem and the solution. Are we failing on two fronts: failing to create the right data-driven cultures, and failing to interpret the data we collect?
The current trend is that new types of data and new types of physical storage are changing all of that.
When I got back from my trip I found a TDWI white paper by Philip Russom that describes the situation very well in a white paper detailing his research on this subject; Evolving Data Warehouse Architectures in the Age of Big Data.
From an enterprise data architecture and management point of view, this is a very interesting paper.
- First the DW architectures are getting complex because of all the new physical storage options available
- Hadoop – very large scale and inexpensive
- NoSQL DBMS – beyond tabular data
- Columnar DBMS – very fast seek time
- DW Appliances – very fast / very expensive
- What is driving these changes is the rapidly-increasing complexity of data. Data volume has captured the imagination of the press, but it is really the rising complexity of the data types that is going to challenge architects.
- But, here is what really jumped out at me. When they asked the people in their survey what are the important components of their data warehouse architecture, the answer came back; Standards and rules. Specifically, they meant how data is modeled, how data quality metrics are created, metadata requirements, interfaces for data integration, etc.
The conclusion for me, from this part of the survey, was that business strategy is requiring more complex data for better analyses (example: realtime response or proactive recommendations) and business processes (example: advanced customer service). This, in turn, is driving IT to look into more advanced technology to deal with different data types and different use cases for the data. And finally, the way they are dealing with the exploding complexity was through standards, particularly data standards. If you are dealing with increasing complexity and have to do it better, faster and cheaper, they only way you are going to survive is by standardizing as much as reasonably makes sense. But, not a bit more.
If you think about it, it is good advice. Get your data standards in place first. It is the best way to manage the data and technology complexity. …And a chance to be the driver rather than the driven.
I highly recommend reading this white paper. There is far more in it than I can cover here. There is also a Philip Russom webinar on DW Architecture that I recommend.
A month ago, I shared that Frank Friedman believes CFOs are “the logical choice to own analytics and put them to work to serve the organization’s needs”. Even though many CFOs are increasingly taking on what could be considered an internal CEO or COO role, many readers protested my post which focused on reviewing Frank Friedman’s argument. At the same time, CIOs have been very clear with me that they do not want to personally become their company’s data steward. So the question becomes should companies be creating a CDO or CAO role to lead this important function? And if yes, how common are these two roles anyway?
Regardless of eventual ownership, extracting value out of data is becoming a critical business capability. It is clear that data scientists should not be shoe horned into the traditional business analyst role. Data Scientists have the unique ability to derive mathematical models “for the extraction of knowledge from data “(Data Science for Business, Foster Provost, 2013, pg 2). For this reason, Thomas Davenport claims that data scientists need to be able to network across an entire business and be able to work at the intersection of business goals, constraints, processes, available data and analytical possibilities. Given this, many organizations today are starting to experiment with the notion of having either a chief data officers (CDOs) or chief analytics officers (CAOs). The open questions is should an enterprise have a CDO or a CAO or both? And as important in the end, it is important to determine where should each of these roles report in the organization?
Data policy versus business questions
In my opinion, it is the critical to first look into the substance of each role before making a decision with regards to the above question. The CDO should be about ensuring that information is properly secured, stored, transmitted or destroyed. This includes, according to COBIT 5, that there are effective security and controls over information systems. To do this, procedures need to be defined and implemented to ensure the integrity and consistency of information stored in databases, data warehouses, and data archives. According to COBIT 5, data governance requires the following four elements:
- Clear information ownership
- Timely, correct information
- Clear enterprise architecture and efficiency
- Compliance and security
To me, these four elements should be the essence of the CDO role. Having said this, the CAO is related but very different in terms of the nature of the role and the business skills require. The CRISP model points out just how different the two roles are. According to CRISP, the CAO role should be focused upon business understanding, data understanding, data preparation, data modeling, and data evaluation. As such the CAO is focused upon using data to solve business problems while the CDO is about protecting data as a business critical asset. I was living in in Silicon Valley during the “Internet Bust”. I remember seeing very few job descriptions and few job descriptions that existed said that they wanted a developer who could also act as a product manager and do some marketing as a part time activity. This of course made no sense. I feel the same way about the idea of combining the CDO and CAO. One is about compliance and protecting data and the other is about solving business problems with data. Peanut butter and chocolate may work in a Reese’s cup but it will not work here—the orientations are too different.
So which business leader should own the CDO and CAO?
Clearly, having two more C’s in the C-Suite creates a more crowded list of corporate officers. Some have even said that this will extended what is called senior executive bloat. And what of course how do these new roles work with and impact the CIO? The answer depends on organization’s culture, of course. However, where there isn’t an executive staff office, I suggest that these roles go to different places. Clearly, many companies already have their CIO function already reporting to finance. Where this is the case, it is important determine whether a COO function is in place. The COO clearly could own the CDO and CAO functions because they have a significant role in improving process processes and capabilities. Where there isn’t a COO function and the CIO reports to the CEO, I think you could have the CDO report to the CIO even though CIOs say they do not want to be a data steward. This could be a third function in parallel the VP of Ops and VP of Apps. And in this case, I would put the CAO report to one of the following: the CFO, Strategy, or IT. Again this all depends on current organizational structure and corporate culture. Regardless of where it reports, the important thing is to focus the CAO on an enterprise analytics capability.
Author Twitter: @MylesSuer
According Michelle Fox of CNBC and Stephen Schork, the oil industry is in ‘dire straits’. U.S. crude posted its ninth-straight weekly loss this week, landing under $50 a barrel. The news is bad enough that it is now expected to lead to major job losses. The Dallas Federal Reserve anticipates that the Texas could lose about 125,000 jobs by the end of June. Patrick Jankowski, an economist and vice president of research at the Greater Houston Partnership, expects exploration budgets will be cut 30-35 percent, which will result in approximately 9,000 fewer wells being drilled. The problem is “if oil prices keep falling, at some point it’s not profitable to pull it out of the ground” (“When, and where, oil is too cheap to be profitable”, CNBC, John W. Schoen).
This means that a portion of the world’s oil supply will become unprofitable to produce. According to Wood Mackenzie, “once the oil price reaches these levels, producers have a sometimes complex decision to continue producing, losing money on every barrel produced, or to halt production, which will reduce supply”. The question are these the only answers?
Major Oil Company Uses Analytics to Gain Business Advantage
A major oil company that we are working with has determined that data is a success enabler for their business. They are demonstrating what we at Informatica like to call a “data ready business”—a business that is ready for any change in market conditions. This company is using next generation analytics to ensure their businesses survival and to make sure they do not become what Jim Cramer likes to call a “marginal producer”. This company has said to us that their success is based upon being able to extract oil more efficiently than its competitors.
Historically data analysis was pretty simple
Traditionally oil producers would get oil by drilling a new hole in the ground. And in 6 months they would start getting the oil flowing commercially and be in business. This meant it would typically take them 6 months or longer before they could get any meaningful results including data that could be used to make broader production decisions.
Drilling from data
Today, oil is, also, produced from shale or fracking techniques. This process can take only 30-60 days before oil producers start seeing results. It is based not just on innovation in the refining of oil, but also on innovation in the refining of data from operational business decisions can be made. The benefits of this approach including the following:
Improved fracking process efficiency
Fracking is a very technical process. Producers can have two wells on the same field that are performing at very different levels of efficiency. To address this issue, the oil company that we have been discussing throughout this piece is using real-time data to optimize its oil extraction across an entire oil field or region. Insights derived from these allow them to compare wells in the same region for efficiency or productivity and even switch off certain wells if the oil price drops below profitability thresholds. This ability is especially important as the price of oil continues to drop. At $70/barrel, many operators go into the red while more efficient data driven operators can remain profitable at $40/barrel. So efficiency is critical across a system of wells.
Using data to decide where to build wells in the first place
When constructing a fracking or sands well, you need more information on trends and formulas to extract oil from the ground. On a site with 100+ wells for example, each one is slightly different because of water tables, ground structure, and the details of the geography. You need the right data, the right formula, and the right method to extract the oil at the best price and not impact the environment at the very same time.
The right technology delivers the needed business advantage
Of course, technology is never been simple to implement. The company we are discussing has 1.2 Petabytes of data they were processing and this volume is only increasing. They are running fiber optic cables down into wells to gather data in real time. As a result, they are receiving vast amounts of real time data but cannot store and analyze the volume of data efficiently in conventional systems. Meanwhile, the time to aggregate and run reports can miss the window of opportunity while increasing cost. Making matters worse, this company had a lot of different varieties of data. It also turns out that quite of bit of the useful information in their data sets was in the comments section of their source application. So traditional data warehousing would not help them to extract the information they really need. They decided to move to new technology, Hadoop. But even seemingly simple problems, like getting access to data were an issue within Hadoop. If you didn’t know the right data analyst, you might not get the data you needed in a timely fashion. Compounding things, a lack of Hadoop skills in Oklahoma proved to be a real problem.
The right technology delivers the right capability
The company had been using a traditional data warehousing environment for years. But they needed help to deal with their Hadoop environment. This meant dealing with the volume, variety and quality of their source well data. They needed a safe, efficient way to integrate all types of data on Hadoop at any scale without having to learn the internals of Hadoop. Early adopters of Hadoop and other Big Data technologies have had no choice but to hand-code using Java or scripting languages such as Pig or Hive. Hiring and retaining big data experts proved time consuming and costly. This is because data scientists and analysts can spend only 20 percent of their time on data analysis and the rest on the tedious mechanics of data integration such as accessing, parsing, and managing data. Fortunately for this oil producer, it didn’t have to be this way. They were able to get away with none of the specialized coding required to scale performance on distributed computing platforms like Hadoop. Additionally, they were able “Map Once, Deploy Anywhere,” knowing that even as technologies change they can run data integration jobs without having to rebuild data processing flows.
It seems clear that we live in an era where data is at the center of just about every business. Data-ready enterprises are able to adapt and win regardless of changing market conditions. These businesses invested in building their enterprise analytics capability before market conditions change. In this case, these oil producers will be able to produce oil at lower costs than others within their industry. Analytics provides three benefits to oil refiners.
- Better margins and lower costs from operations
- Lowers risk of environmental impact
- Lower time to build a successful well
In essence, those that build analytics as a core enterprise capability will continue to have a right to win within a dynamic oil pricing environment.
Analytics Stories: A Banking Case Study
Analytics Stories: A Financial Services Case Study
Analytics Stories: A Healthcare Case Study
Who Owns Enterprise Analytics and Data?
Competing on Analytics: A Follow Up to Thomas H. Davenport’s Post in HBR
Thomas Davenport Book “Competing On Analytics”
Like me, you probably just returned from an inspiring Sales Kick Off 2015 event. You’ve invested in talented people. You’ve trained them with the skills and knowledge they need to identify, qualify, validate, negotiate and close deals. You’ve invested in world-class applications, like Salesforce Sales Cloud, to empower your sales team to sell more effectively. But does your sales team have what they need to succeed in 2015?
Gartner predicts that as early as next year, companies will compete primarily on the customer experiences they deliver. So, every customer interaction counts. Knowing your customers is key to delivering great sales experiences.
But, inaccurate, inconsistent and disconnected customer information may be holding your sales team back from delivering great sales experiences. If you’re not fueling Salesforce Sales Cloud (or another Sales Force Automation (SFA) application) with clean, consistent and connected customer information, your sales team may be at a disadvantage against the competition.
To successfully compete and deliver great sales experiences more efficiently, your sales team needs a complete picture of their customers. They don’t want to pull information from multiple applications and then reconcile it in spreadsheets. They want direct access to the Total Customer Relationship across channels, touch points and products within their Salesforce Sales Cloud.
Watch this short video comparing a day-in-the-life of two sales reps competing for the same business. One has access to the Total Customer Relationship in Salesforce Sales Cloud, the other does not. Watch now: Salesforce.com with Clean, Consistent and Connected Customer Information.
Is your sales team spending time creating spreadsheets by pulling together customer information from multiple applications and then reconciling it to understand the Total Customer Relationship across channels, touch points and products? If so, how much is it costing your business? Or is your sales team engaging with customers without understanding the Total Customer Relationship? How much is that costing your business?
Many innovative sales leaders are gaining a competitive edge by better leveraging their customer data to empower their sales teams to deliver great sales experiences. They are fueling business and analytical applications, like Salesforce Sales Cloud, with clean, consistent and connected customer information. They are arming their sales teams with direct access to richer customer profiles, which includes the Total Customer Relationship across channels, touch points and products.
What measurable results have these sales leaders acheived? Merrill Lynch boosted sales productivity by 15%, resulting in $50M in annual impact. A $60B manufacturing company improved cross-sell and up-sell success by 5%. Logitech increased across channels: online, in their retail partner’s stores and through distribution partners.
This year, I believe more sales leaders will focus on leveraging their customer information for competitive advantage. This will help them shift from sales automation to sales optimization. What do you think?
In my previous blog, I talked about how a business-led approach can displace technology-led projects. Historically IT-led projects have invested significant capital while returning minimal business value. It further talks about how transformation roadmap execution is sustainable because the business is driving the effort where initiative investments are directly traceable to priority business goals.
For example, an insurance company wants to improve the overall customer experience. Mature business architecture will perform an assessment to highlight all customer touch points. It requires a detailed capability map, fully formed, customer-triggered value streams, value stream/ capability cross-mappings and stakeholder/ value stream cross-mappings. These business blueprints allow architects and analysts to pinpoint customer trigger points, customer interaction points and participating stakeholders engaged in value delivery.
One must understand that value streams and capabilities are not tied to business unit or other structural boundaries. This means that while the analysis performed in our customer experience example may have been initiated by a given business unit, the analysis may be universally applied to all business units, product lines and customer segments. Using the business architecture to provide a representative cross-business perspective requires incorporating organization mapping into the mix.
Incorporating the application architecture into the analysis and proposed solution is simply an extension of business architecture mapping that incorporates the IT architecture. Robust business architecture is readily mapped to the application architecture, highlighting enterprise software solutions that automate various capabilities, which in turn enable value delivery. Bear in mind, however, that many of the issues highlighted through a business architecture assessment may not have corresponding software deployments since significant interactions across the business tend to be manual or desktop-enabled. This opens the door to new automation opportunities and new ways to think about business design solutions.
Building and prioritizing the transformation strategy and roadmap is dramatically simplified once all business perspectives needed to enhance customer experience are fully exposed. For example, if customer service is a top priority, then that value stream becomes the number one target, with each stage prioritized based on business value and return on investment. Stakeholder mapping further refines design approaches for optimizing stakeholder engagement, particularly where work is sub-optimized and lacks automation.
Capability mapping to underlying application systems and services provides the basis for establishing a corresponding IT deployment program, where the creation and reuse of standardized services becomes a focal point. In certain cases, a comprehensive application and data architecture transformation becomes a consideration, but in all cases, any action taken will be business and not technology driven.
Once this occurs, everyone will focus on achieving the same goals, tied to the same business perspectives, regardless of the technology involved.
As you may know, COSO provides the overarching enterprise framework for corporate governance. This includes operations, reporting, and compliance. A key objective for COSO is the holding of individuals accountable for their internal control responsibilities. The COSO process typically starts by accessing risks and developing sets of control activities to mitigate discovered risks.
On an ongoing basis, organizations then need as well to generate relevant, quality information to evaluate the functioning of established internal controls. And finally they need to select, develop, and perform ongoing evaluations to ascertain whether the internal controls are present and functioning appropriately. Having said all of this, the COSO framework will not be effective without first having established effective Information and Data Governance.
So you might be asking yourself as a corporate officer why should you care about this topic anyway. Isn’t this the job of the CIO or that new person, the CDO? The answer is no. Today’s enterprises are built upon data and analytics. The conundrum here is that “you can’t be analytical without data and you can’t be really good at analytics without really good data”. (Analytics at Work, Thomas Davenport, Harvard Business Review Press, page 23). What enterprises tell us they need is great data—data which is clean, safe, and increasingly connected. And yes, the CIO is going to make this happen for you, but they are not going to do this appropriately without the help of data stewards that you select from your business units. These stewards need to help the CIO or CDO determine what data matters to the enterprise. What data should be secured? And finally, they will determine what data, information, and knowledge will drive the business right to win on an ongoing basis.
So now that you know why your involvement matters, I need to share that this control activity is managed by a supporting standard to COSO, COBIT 5. To learn specifically about what COBIT 5 recommends for Information and Data Governance, please click and read an article from the latest COBIT Focus entitled “Using COBIT 5 to Deliver Information and Data Governance”.
As I have shared within other posts within this series, businesses are using analytics to improve their internal and external facing business processes and to strengthen their “right to win” within the markets that they operate. For pharmaceutical businesses, strengthening the right to win begins and ends with the drug product development lifecycle. I remember, for example, talking several years ago to the CFO of major pharmaceutical company and having him tell me the most important financial metrics for him had to do with reducing the time to market for a new drug and maximizing the period of patent protection. Clearly, the faster a pharmaceutical company gets a product to market, the faster it can begin to earning a return on its investment.
Fragmented data challenged analytical efforts
At Quintiles, what the business needed was a system with the ability to optimize design, execution, quality, and management of clinical trials. Management’s goal was to dramatically shorten time to complete each trial, including quickly identifying when a trial should be terminated. At the same time, management wanted to continuously comply with regulatory scrutiny from Federal Drug Administration and use it to proactively monitor and manage notable trial events.
The problem was Quintiles data was fragmented across multiple systems and this delayed the ability to make business decisions. Like many organizations, Quintiles data was located in multiple incompatible legacy systems. This meant there was extensive manual data manipulation before data could become useful. As well, incompatible legacy systems impeded data integration and normalization, and prohibited a holistic view across all sources. Making matters worse, management felt that it lacked the ability to take corrective actions in a timely manner.
Infosario launched to manage Quintiles analytical challenges
To address these challenges, Quintiles leadership launched the Infosario Clinical Data Management Platform to power its pharmaceutical product development process. Infosario breaks down the silos of information that have limited combining massive quantities of scientific and operational data collected during clinical development with tens of millions of real-world patient records and population data. This step empowered researchers and drug developers to unlock a holistic view of data. This improved decision-making, and ultimately increasing the probability of success at every step in a product’s lifecycle. Quintiles Chief Information Officer, Richard Thomas says, “The drug development process is predicated upon the availability of high quality data with which to collaborate and make informed decisions during the evolution of a product or treatment”.
What Quintiles has succeeded in doing with Infosario is the integration of data and processes associated with a drug’s lifecycle. This includes creating a data engine to collect, clean, and prepare data for analysis. The data is then combined with clinical research data and information from other sources to provide a set of predictive analytics. This of course is aimed at impacting business outcomes.
The Infosario solution consists of several core elements
At its core, Infosario provides the data integration and data quality capabilities for extracting and organizing clinical and operational data. The approach combines and harmonizes data from multiple heterogeneous sources into what is called the Infosario Data Factory repository. The end is to accelerate reporting. Infosario leverages data federation /virtualization technologies to acquire information from disparate sources in a timely manner without affecting the underlying foundational enterprise data warehouse. As well, it implements a rule-based, real-time intelligent monitoring and alerting to enable the business to tweak and enhance business processes as they are needed. A “monitoring and alerting layer” sits on top of the data, with the facility to rapidly provide intelligent alerts to appropriate stakeholders regarding trial-related issues and milestone events. Here are some more specifics on the components of the Infosario solution:
• Data Mastering provides the capability to link multi-domains of data. This enables enterprise information assets to be actively managed, with an integrated view of the hierarchies and relationships.
• Data Management provides the high performance, scalable data integration needed to support enterprise data warehouses and critical operational data stores.
• Data Services provides the ability to combine data from multiple heterogeneous data sources into a single virtualized view. This allows Infosario to utilize data services to accelerate delivery of needed information.
• Complex Event Processing manages the critical task of monitoring enterprise data quality events and delivering alerts to key stakeholders to take necessary action.
According to Richard Thomas, “the drug development process rests on the high quality data being used to make informed decisions during the evolution of a product or treatment. Quintiles’ Infosario clinical data management platform gives researchers and drug developers with the knowledge needed to improve decision-making and ultimately increase the probability of success at every step in a product’s lifecycle.” This it enables enhanced data accuracy, timeliness, and completeness. On the business side, it has enables Quintiles to establish industry-leading information and insight. And this in turn has enables the ability to make faster, more informed decisions, and to take action based on insights. This importantly has led to a faster time to market and a lengthening of the period of patent protection.
Analytics Stories: A Banking Case Study
Analytics Stories: A Financial Services Case Study
Analytics Stories: A Healthcare Case Study
Who Owns Enterprise Analytics and Data?
Competing on Analytics: A Follow Up to Thomas H. Davenport’s Post in HBR
Thomas Davenport Book “Competing On Analytics”
Solution Brief: The Intelligent Data Platform
Author Twitter: @MylesSuer