Category Archives: Data Quality

If Data Projects Weather, Why Not Corporate Revenue?

Every fall Informatica sales leadership puts together its strategy for the following year.  The revenue target is typically a function of the number of sellers, the addressable market size and key accounts in a given territory, average spend and conversion rate given prior years’ experience, etc.  This straight forward math has not changed in probably decades, but it assumes that the underlying data are 100% correct. This data includes:

  • Number of accounts with a decision-making location in a territory
  • Related IT spend and prioritization
  • Organizational characteristics like legal ownership, industry code, credit score, annual report figures, etc.
  • Key contacts, roles and sentiment
  • Prior interaction (campaign response, etc.) and transaction (quotes, orders, payments, products, etc.) history with the firm

Every organization, no matter if it is a life insurer, a pharmaceutical manufacturer, a fashion retailer or a construction company knows this math and plans on getting somewhere above 85% achievement of the resulting target.  Office locations, support infrastructure spend, compensation and hiring plans are based on this and communicated.

data revenue

We Are Not Modeling the Global Climate Here

So why is it that when it is an open secret that the underlying data is far from perfect (accurate, current and useful) and corrupts outcomes, too few believe that fixing it has any revenue impact?  After all, we are not projecting the climate for the next hundred years here with a thousand plus variables.

If corporate hierarchies are incorrect, your spend projections based on incorrect territory targets, credit terms and discount strategy will be off.  If every client touch point does not have a complete picture of cross-departmental purchases and campaign responses, your customer acquisition cost will be too high as you will contact the wrong prospects with irrelevant offers.  If billing, tax or product codes are incorrect, your billing will be off.  This is a classic telecommunication example worth millions every month.  If your equipment location and configuration is wrong, maintenance schedules will be incorrect and every hour of production interruption will cost an industrial manufacturer of wood pellets or oil millions.

Also, if industry leaders enjoy an upsell ratio of 17%, and you experience 3%, data (assuming you have no formal upsell policy as it violates your independent middleman relationship) data will have a lot to do with it.

The challenge is not the fact that data can create revenue improvements but how much given the other factors: people and process.

Every industry laggard can identify a few FTEs who spend 25% of their time putting one-off data repositories together for some compliance, M&A customer or marketing analytics.  Organic revenue growth from net-new or previously unrealized revenue is what the focus of any data management initiative should be.  Don’t get me wrong; purposeful recruitment (people), comp plans and training (processes) are important as well.  Few people doubt that people and process drives revenue growth.  However, few believe data being fed into these processes has an impact.

This is a head scratcher for me. An IT manager at a US upstream oil firm once told me that it would be ludicrous to think data has a revenue impact.  They just fixed data because it is important so his consumers would know where all the wells are and which ones made a good profit.  Isn’t that assuming data drives production revenue? (Rhetorical question)

A CFO at a smaller retail bank said during a call that his account managers know their clients’ needs and history. There is nothing more good data can add in terms of value.  And this happened after twenty other folks at his bank including his own team delivered more than ten use cases, of which three were based on revenue.

Hard cost (materials and FTE) reduction is easy, cost avoidance a leap of faith to a degree but revenue is not any less concrete; otherwise, why not just throw the dice and see how the revenue will look like next year without a central customer database?  Let every department have each account executive get their own data, structure it the way they want and put it on paper and make hard copies for distribution to HQ.  This is not about paper versus electronic but the inability to reconcile data from many sources on paper, which is a step above electronic.

Have you ever heard of any organization move back to the Fifties and compete today?  That would be a fun exercise.  Thoughts, suggestions – I would be glad to hear them?

FacebookTwitterLinkedInEmailPrintShare
Posted in Banking & Capital Markets, Big Data, Business Impact / Benefits, Business/IT Collaboration, Data Governance, Data Integration, Data Quality, Data Warehousing, Enterprise Data Management, Governance, Risk and Compliance, Master Data Management, Product Information Management | Tagged , | Leave a comment

Making Competing on Analytics Reality: A Case Study From UMass Memorial Healthcare

As I indicated in Competing on Analytics, if you ask CIOs today about the importance of data to their enterprises, they will likely tell you about their business’ need to “compete on analytics”, to deliver better business insights, and to drive faster business decision making. These have a high place on the business and CIO agendas, according to Thomas H. Davenport, because “at a time when firms in many industries offer similar products and use comparable technologies, business processes are among the last remaining points of differentiation.” For this reason, Davenport claims timely analytics enables companies to “wring every last drop of value from their processes”.

So is anyone showing the way on how to compete on analytics?

healthcareUMass Memorial Health Care is a great example of an enterprise that is using analytics to “wring every last drop of value from their processes”. However, before UMass could compete on data, it needed to create data that could be trusted by its leadership team.

Competing on analytics requires trustworthy data

TrustworthyAt UMass, they found that they could not accurately measure the size of their patient care population. This is a critical metric for growing market share. Think about how hard it would be to operate any business without an accurate count of how many customers are being served. Lacking this information hindered UMass’ ability to make strategic market decisions and drive key business and clinical imperatives.

A key need at UMASS was to determine a number of critical success factors for its business. This included obviously the size of the patient population but it also included the composition of the patient population and the number of unique patients served by primary care physician providers across each of its business locations. Without this knowledge, UMASS found itself struggling to make effective decisions regarding its strategic direction, its clinical policies, and even its financial management. And all of these factors really matter in an era of healthcare reform.

Things proved particularly complex at UMass since they act as what is called a “complex integrated delivery network”. This means that portions of its business effectively operated under different business models. This, however, creates a data challenge in healthcare. Unlike other diversified enterprises, UMASS needs an operating model–“the necessary level of business process integration and standardization for delivering its services to customers”[1]– that could support different elements of its business but be unified for integrative analysis. This matters because in UMass’ case, there is a single denominator, the patient. And to be clear, while each of UMASS’ organizations could depend on their data to meet their needs, UMASS lacked an integrative view into patients.

Departmental Data may be good for a department but not for the Enterprise

Data AnalysisUMass had adequate data for each organization, such as delivering patient care or billing for a specific department or hospital, but it was inadequate for system wide measures. And aggregation and analytics, which needed to combine data across systems and organizations was stymied by data inconsistencies, incomplete population of fields, or other types of data quality problems between each system. These issues made it impossible to provide the analytics UMass’ senior managers needed. For example, UMass’ aggregated data contained duplicate patients—people who had been treated at different sites and had different medical record numbers, but who were in fact the same patients.

A key need for UMass creating the ability to compete on analytics was to measure and report on the number of primary care patients being treated across their entire healthcare system. UMass leadership saw this as a key planning and strategy metric because primary care patients today are the focus of investments in wellness and prevention programs, as well as a key source of specialty visits and inpatients. According to George Brenckle, Senior Vice President and CIO, they “had an urgent need for improved clinical and business intelligence across all our operations, we needed an integrated view of patient information, encounters, providers, and UMass Memorial locations to support improved decision making, advance the quality of patient care, and increase patient loyalty. To put the problem into perspective, we have more than 100 applications—some critical, some not so critical—and our ultimate ambition was to integrate all of these areas of business, leverage analytics, and drive clinical and operational excellence.”

The UMASS Solution

solutionThe UMass solved the above issues by creating an integrated way to view of patient information, encounters, providers, and UMass Memorial locations. This allowed UMass to compute the number of primary care physician patients cared for. In order to make this work, the solution merged data from the core hospital information applications and processed this data for quality issue that prevented UMass from deriving the primary care patient count. Armed with this, data integration helped UMass Memorial improve its clinical outcomes, grow its patient population, increase process efficiency, and ultimately maximize its return on data. As well UMASS gained a reliable measure of its primary care patient population, UMASS now was able to determine an accurate counts for unique patients served by its hospitals (3.2 million), active patients (i.e., those treated within the last three years—approximately 1.7 million), and unique providers (approximately 24,000).

According to Brenckle, data integration transformed their analytical capabilities and decision making. “We know who our primary care patients are and how many there are of them, whether the volume of patients is rising or decreasing, how many we are treating in an ambulatory or acute care setting, and what happens to those patients as they move through the healthcare system. We are able to examine which providers they saw and at which location. This data is vital to improving clinical outcomes, growing the patient population, and increasing efficiency.”

Related links

Solution Brief: The Intelligent Data Platform
Details on the UMASS Solution

Related Blogs

Thomas Davenport Book “Competing On Analytics”
Competing on Analytics
The Business Case for Better Data Connectivity
CIO explains the importance of Big Data to Healthcare
The CFO Viewpoint upon Data
What an enlightened healthcare CEO should tell their CIO?
Twitter: @MylesSuer

 


 

[1] Enterprise Architecture as Business Strategy, Jeanne Ross, Harvard Business School Press, Page 8
FacebookTwitterLinkedInEmailPrintShare
Posted in CIO, Data Governance, Data Quality | Tagged , , , | Leave a comment

More Evidence That Data Integration Is Clearly Strategic

Data Integration Is Clearly Strategic

Data Integration Is Strategic

A recent study from Epicor Software Corporation surveyed more than 300 IT and business decision-makers.  The study results highlighted the biggest challenges and opportunities facing Australian businesses. The independent research report “From Business Processes to Product Distribution” was based upon a survey of Australian organizations with more than 20 employees.

Key findings from the report include:

  • 65% of organizations cite data processing and integration as hampering distribution capability, with nearly half claiming their existing software and ERP is not suitable for distribution.
  • Nearly two-thirds of enterprises have some form of distribution process, involving products or services.
  • More than 80% of organizations have at least some problem with product or service distribution.
  • More than 50% of CIOs in organizations with distribution processes believe better distribution would increase revenue and optimize business processes, with a further 38% citing reduced operating costs.

The core findings: “With better data integration comes better automation and decision making.”

This report is one of many I’ve seen over the years that come to the same conclusion.  Most of those involved with the operations of the business don’t have access to key data points they need, thus they can’t automate tactical decisions, and also cannot “mine” the data, in terms of understanding the true state of the business.

The more businesses deal with building and moving products, the more data integration becomes an imperative value.  As stated in this survey, as well as others, the large majority cite “data processing and integration as hampering distribution capabilities.”

Of course, these issues goes well beyond Australia.  Most enterprises I’ve dealt with have some gap between the need to share key business data to support business processes, and decision support, and what current exists in terms of data integration capabilities.

The focus here is on the multiple values that data integration can bring.  This includes:

  • The ability to track everything as it moves from manufacturing, to inventory, to distribution, and beyond.  You to bind these to core business processes, such as automatic reordering of parts to make more products, to fill inventory.
  • The ability to see into the past, and to see into the future.  The emerging approaches to predictive analytics allow businesses to finally see into the future.  Also, to see what went truly right and truly wrong in the past.

While data integration technology has been around for decades, most businesses that both manufacture and distribute products have not taken full advantage of this technology.  The reasons range from perceptions around affordability, to the skills required to maintain the data integration flow.  However, the truth is that you really can’t afford to ignore data integration technology any longer.  It’s time to create and deploy a data integration strategy, using the right technology.

This survey is just an instance of a pattern.  Data integration was considered optional in the past.  With today’s emerging notions around the strategic use of data, clearly, it’s no longer an option.

FacebookTwitterLinkedInEmailPrintShare
Posted in Data First, Data Integration, Data Integration Platform, Data Quality | Tagged , , , | Leave a comment

At Valspar Data Management is Key to Controlling Purchasing Costs

Steve Jenkins, Global IT Director at Valspar

Steve Jenkins is working to improve information management maturity at Valspar

Raw materials costs are the company’s single largest expense category,” said Steve Jenkins, Global IT Director at Valspar, at MDM Day in London. “Data management technology can help us improve business process efficiency, manage sourcing risk and reduce RFQ cycle times.”

Valspar is a $4 billion global manufacturing company, which produces a portfolio of leading paint and coating brands. At the end of 2013, the 200 year old company celebrated record sales and earnings. They also completed two acquisitions. Valspar now has 10,000 employees operating in 25 countries.

As is the case for many global companies, growth creates complexity. “Valspar has multiple business units with varying purchasing practices. We source raw materials from 1,000s of vendors around the globe,” shared Steve.

“We want to achieve economies of scale in purchasing to control spending,” Steve said as he shared Valspar’s improvement objectives. “We want to build stronger relationships with our preferred vendors. Also, we want to develop internal process efficiencies to realize additional savings.”

Poorly managed vendor and raw materials data was impacting Valspar’s buying power

Data management at Valspar

“We realized our buying power was limited by the age and quality of available vendor and raw materials data.”

The Valspar team, who sharply focuses on productivity, had an “Aha” moment. “We realized our buying power was limited by the age and quality of available vendor data and raw materials data,” revealed Steve. 

The core vendor data and raw materials data that should have been the same across multiple systems wasn’t. Data was often missing or wrong. This made it difficult to calculate the total spend on raw materials. It was also hard to calculate the total cost of expedited freight of raw materials. So, employees used a manual, time-consuming and error-prone process to consolidate vendor data and raw materials data for reporting.

These data issues were getting in the way of achieving their improvement objectives. Valspar needed a data management solution.

Valspar needed a single trusted source of vendor and raw materials data

Informatica MDM supports vendor and raw materials data management at Valspar

The team chose Informatica MDM as their enterprise hub for vendors and raw materials

The team chose Informatica MDM, master data management (MDM) technology. It will be their enterprise hub for vendors and raw materials. It will manage this data centrally on an ongoing basis. With Informatica MDM, Valspar will have a single trusted source of vendor and raw materials data.

Informatica PowerCenter will access data from multiple source systems. Informatica Data Quality will profile the data before it goes into the hub. Then, after Informatica MDM does it’s magic, PowerCenter will deliver clean, consistent, connected and enriched data to target systems.

Better vendor and raw materials data management results in cost savings

Valspar Chameleon Jon

Valspar will gain benefits by fueling applications with clean, consistent, connected and enriched data

Valspar expects to gain the following business benefits:

  • Streamline the RFQ process to accelerate raw materials cost savings
  • Reduce the total number of raw materials SKUs and vendors
  • Increase productivity of staff focused on pulling and maintaining data
  • Leverage consistent global data visibly to:
    • increase leverage during contract negotiations
    • improve acquisition due diligence reviews
    • facilitate process standardization and reporting

 

Valspar’s vision is to tranform data and information into a trusted organizational assets

“Mastering vendor and raw materials data is Phase 1 of our vision to transform data and information into trusted organizational assets,” shared Steve. In Phase 2 the Valspar team will master customer data so they have immediate access to the total purchases of key global customers. In Phase 3, Valspar’s team will turn their attention to product or finished goods data.

Steve ended his presentation with some advice. “First, include your business counterparts in the process as early as possible. They need to own and drive the business case as well as the approval process. Also, master only the vendor and raw materials attributes required to realize the business benefit.”

Total Supplier Information Management eBook

Click here to download the Total Supplier Information Management eBook

Want more? Download the Total Supplier Information Management eBook. It covers:

  • Why your fragmented supplier data is holding you back
  • The cost of supplier data chaos
  • The warning signs you need to be looking for
  • How you can achieve Total Supplier Information Management

 

FacebookTwitterLinkedInEmailPrintShare
Posted in Business/IT Collaboration, Data Integration, Data Quality, Manufacturing, Master Data Management, Operational Efficiency, PowerCenter, Vertical | Tagged , , , , , , , , , , , , , , , , , , | Leave a comment

Scalable Enterprise Analytics: Informatica PowerCenter Data Quality and Oracle Exadata

In 2012, Forbes published an article predicting an upcoming problem.

The Need for Scalable Enterprise Analytics

Specifically, increased exploration in Big Data opportunities would place pressure on the typical corporate infrastructure. The generic hardware used to run most tech industry enterprise applications was not designed to handle real-time data processing. As a result, the explosion of mobile usages, and the proliferation of social networks, was increasing the strain on the system. Most companies now faced real-time processing requirements beyond what the traditional model was designed to handle.

In the past two years, the volume of data and speed of data growth has grown significantly. As a result, the problem has become more severe. It is now clear that these challenges can’t be overcome by simply doubling or tripling their IT spending on infrastructure sprawl. Today, enterprises seek consolidated solutions that offer scalability, performance and ease of administration. The present need is for scalable enterprise analytics.

A Clear Solution Is Available

Informatica PowerCenter and Data Quality is the market leading data integration and data quality platform. This platform has now been certified by Oracle as an optimal solution for both the Oracle Exadata Database Machine and the Oracle SuperCluster.

As the high-speed on-ramp for data into Oracle Exadata, PowerCenter and Data Quality deliver up-to five times faster performance on data load, query, profiling and cleansing tasks. Informatica’s data integration customers can now easily reuse data integration code, skills and resources to access and transform any data from any data source and load it into Exadata, with the highest throughput and scalability.

Customers adopting Oracle Exadata for high-volume, high-speed analytics can now be confident with Informatica PowerCenter and Data Quality. With these products, they can ingest, cleanse and transform all types of data into Exadata with the highest performance and scale required to maximize the value of their Exadata investment.

Proving the Value of Scalable Enterprise Analytics

In order to demonstrate the efficacy of their partnership, the two companies worked together on a Proof Of Value (POV) project. The goal is to prove that using PowerCenter with Exadata would improve both performance and scalability. The project involved PowerCenter and Data Quality 9.6.1 and x4-2 Exadata Machine. Oracle 11g was considered for both standard Oracle and Exadata versions.

The first test conducted a 1TB load test to Exadata and standard Oracle in a typical PowerCenter use case. The second test consisted of querying 1TB profiling warehouse database in Data Quality use case scenario. Performance data was collected for both tests. The scalability factor was also captured. A variant of the TPCH dataset was used to generate the test data. The results were significantly higher than prior Exabyte 1TB test. In particular:

  • The data query tests achieved 5x performance.
  • The data load tests achieved a 3x-5x speed increase.
  • Linear scalability was achieved with read/write tests on Exadata.

What Business Benefits Could You Expect?

Informatica PowerCenter and Data Quality, along-with Oracle Exadata, now provide the best-of-breed combination of software and hardware, optimized to deliver the highest possible total system performance. These comprehensive tools drive agile reporting and analytics, while empowering IT organizations to meet SLAs and quality goals like never before.

  1. Extend Oracle Exadata’s access to even more business critical data sources. Utilize optimized out-of-the-box Informatica connectivity to easily access hundreds of data sources, including all the major databases, on-premise and cloud applications, mainframe, social data and Hadoop.
  2. Get more data, more quickly into Oracle Exadata. Move higher volumes of trusted data quickly into Exadata to support timely reporting with up-to-date information (i.e. up to 5x performance improvement compared to Oracle database).
  3.  Centralize management and improve insight into large scale data warehouses. Deliver the necessary insights to stakeholders with intuitive data lineage and a collaborative business glossary. Contribute to high quality business analytics, in a timely manner across the enterprise.
  4. Instantly re-direct workloads and resources to Oracle Exadata without compromising performance. Leverage existing code and programming skills to execute high-performance data integration directly on Exadata by performing push down optimization.
  5. Roll-out data integration projects faster and more cost-effectively. Customers can now leverage thousands of Informatica certified developers to execute existing data integration and quality transformations directly on Oracle Exadata, without any additional coding.
  6. Efficiently scale-up and scale-out. Customers can now maximize performance and lower the costs of data integration and quality operations of any scale by performing Informatica workload and push down optimization on Oracle Exadata.
  7.  Save significant costs involved in administration and expansion. Customers can now easily and economically manage large-scale analytics data warehousing environments with a single point of administration and control, and consolidate a multitude of servers on one rack.
  8.  Reduce risk. Customers can now leverage Informatica’s data integration and quality platform to overcome the typical performance and scalability limitations seen in databases and data storage systems. This will help reduce quality-of-service risks as data volumes rise.

Conclusion

Oracle Exadata is a well-engineered system that offers customers out-of-box scalability and performance on demand.  Informatica PowerCenter and Data Quality are optimized to run on Exadata, offering customers business benefits that speed up data integration and data quality tasks like never before.  Informatica’s certified, optimized, and purpose-built solutions for Oracle can help you enable more timely and trustworthy reporting.  You can now benefit from Informatica’s optimized solutions for Oracle Exadata to make better business decisions by unlocking the full potential of the most current and complete enterprise data available. As shown in our test results, you can attain up to 5x performance by scaling Exadata. Informatica Data Quality customers can perform profiling 1TB datasets, which is unheard before. We urge you to deploy the combined solution to solve your data integration and quality problems today while achieving high speed business analytics in these days of big data exploration and Internet Of Things.

Note:

Listen to what Ash Kulkarni, SVP, at OOW14 has to say on how @InformaticaCORP PowerCenter and Data Quality certified by Oracle as optimized for Exadata can deliver up-to five times faster performance improvement on data load, query, profiling, cleansing and mastering tasks, for Exadata.

FacebookTwitterLinkedInEmailPrintShare
Posted in Data Integration, Data Integration Platform, Data Quality, Data Services, Data Warehousing, Enterprise Data Management, PowerCenter, Vibe | Tagged | Leave a comment

CFO Move to Chief Profitability Officer

30% or higher of each company’s businesses are unprofitable

cfoAccording to Jonathan Brynes at the MIT Sloan School, “the most important issue facing most managers …is making more money from their existing businesses without costly new initiatives”. In Brynes’ cross industry research, he found that 30% or higher of each company’s businesses are unprofitable. Brynes claims these business losses are offset by what are “islands of high profitability”. The root cause of this issue is asserted to be the inability of current financial and management control systems to surface profitability problems and opportunities. Why is this the case? Byrnes believes that management budgetary guidance by its very nature assumes the continuation of the status quo. For this reason, the response to management asking for a revenue increase is to increase revenues for businesses that are profitable and unprofitable. Given this, “the areas of embedded unprofitability remain embedded and largely invisible”. At the same time to be completely fair, it should be recognized that it takes significant labor to accurately and completely put together a complete picture on direct and indirect costs.

The CFO needs to become the point person on profitability issues

cfo

Byrnes believes, nevertheless, that CFOs need to become the corporate point person for surfacing profitability issues. They, in fact, should act as the leader of a new and important role, the chief profitability officer. This may seem like an odd suggestion since virtually every CFO if asked would view profitability as a core element of their job. But Byrnes believes that CFOs need to move beyond broad, departmental performance measures and build profitability management processes into their companies’ core management activities. This task requires the CFO to determine two things.

  1. Which product lines, customers, segments, and channels are unprofitable so investments can be reduced or even eliminated?
  2. Which product lines, customers, segments, and channels are the most profitable so management can determine whether to expand investments and supporting operations?

Why didn’t portfolio management solve this problem?

cfoNow as a strategy MBA, Byrnes’ suggestion leave me wondering why the analysis proposed by strategy consultants like Boston Consulting Group didn’t solve this problem a long time ago. After all portfolio analysis has at its core the notion that relative market share and growth rate will determine profitability and which businesses a firm should build share, hold share, harvest share, or divest share—i.e. reduce, eliminate, or expand investment. The truth is getting at these figures, especially profitability, is a time consuming effort.

KPMG finds 91% of CFOs are held back by financial and performance systems

KPMG

As financial and business systems have become more complex, it has become harder and harder to holistically analyze customer and product profitability because the relevant data is spread over a myriad of systems, technologies, and locations. For this reason, 91% of CFO respondents in a recent KPMG survey said that they want to improve the quality of their financial and performance insight from the data they produce. An amazing 51% of these CFOs, also, admitted that the “collection, storage, and retrieval financial and performance data at their company is primarily a manual and/or spreadsheet-based exercise”. Think about it — a majority of these CFOs teams time is spent collecting financial data rather than actively managing corporate profitability.

How do we fix things?

FixWhat is needed is a solution that allows financial teams to proactively produce trustworthy financial data from each and every financial system and then reliably combine and aggregate the data coming from multiple financial systems. Having accomplished this, the solution needs to allow financial organizations to slice and dice net profitability for product lines and customers.

This approach would not only allow financial organizations to cut their financial operational costs but more importantly drive better business profitability by surfacing profitability gaps. At the same time, it would enable financial organizations to assist business units in making more informed customer and product line investment decisions. If a product line or business is narrowly profitable and lacks a broader strategic context or ability to increase profitability by growing market share, it is a candidate for investment reduction or elimination.

Strategic CFOs need to start asking questions of their business counterparts starting with their justification for their investment strategy. Key to doing this involves consolidating reliable profitability data across customers, products, channel partners, suppliers. This would eliminate the time spent searching for and manually reconciling data in different formats across multiple systems. It should deliver ready analysis across locations, applications, channels, and departments.

Some parting thoughts

Strategic CFOs tell us they are trying to seize the opportunity “to be a business person versus a bean counting historically oriented CPA”. I believe a key element of this is seizing the opportunity to become the firm’s chief profitability officer. To do this well, CFOs need dependable data that can be sliced and diced by business dimensions. Armed with this information, CFOs can determine the most and least profitability, businesses, product lines, and customers. As well, they can come to the business table with the perspective to help guide their company’s success.

Related links
Solution Brief: The Intelligent Data Platform
Related Blogs
CFOs Discuss Their Technology Priorities
The CFO Viewpoint upon Data
How CFOs can change the conversation with their CIO?
New type of CFO represents a potent CIO ally
Competing on Analytics
The Business Case for Better Data Connectivity

Twitter: @MylesSuer

FacebookTwitterLinkedInEmailPrintShare
Posted in Business Impact / Benefits, Business/IT Collaboration, CIO, Data Governance, Data Quality | Tagged , , , , , | Leave a comment

Once Again, Data Integration Proves Critical to Data Analytics

When it comes to cloud-based data analytics, a recent study by Ventana Research (as found in Loraine Lawson’s recent blog post) provides a few interesting data points.  The study reveals that 40 percent of respondents cited lowered costs as a top benefit, improved efficiency was a close second at 39 percent, and better communication and knowledge sharing also ranked highly at 34 percent.

Ventana Research also found that organizations cite a unique and more complex reason to avoid cloud analytics and BI.  Legacy integration work can be a major hindrance, particularly when BI tools are already integrated with other applications.  In other words, it’s the same old story:

You can’t make sense of data that you can’t see.

Data Integration Proves Critical to Data Analytics

Data Integration is Critical to Data Analytics

The ability to deal with existing legacy systems when moving to concepts such as big data or cloud-based analytics is critical to the success of any enterprise data analytics strategy.  However, most enterprises don’t focus on data integration as much as they should, and hope that they can solve the problems using ad-hoc approaches.

These approaches rarely work as well a they should, if at all.  Thus, any investment made in data analytics technology is often diminished because the BI tools or applications that leverage analytics can’t see all of the relevant data.  As a result, only part of the story is told by the available data, and those who leverage data analytics don’t rely on the information, and that means failure.

What’s frustrating to me about this issue is that the problem is easily solved.  Those in the enterprise charged with standing up data analytics should put a plan in place to integrate new and legacy systems.  As part of that plan, there should be a common understanding around business concepts/entities of a customer, sale, inventory, etc., and all of the data related to these concepts/entities must be visible to the data analytics engines and tools.  This requires a data integration strategy, and technology.

As enterprises embark on a new day of more advanced and valuable data analytics technology, largely built upon the cloud and big data, the data integration strategy should be systemic.  This means mapping a path for the data from the source legacy systems, to the views that the data analytics systems should include.  What’s more, this data should be in real operational time because data analytics loses value as the data becomes older and out-of-date.  We operate a in a real-time world now.

So, the work ahead requires planning to occur at both the conceptual and physical levels to define how data analytics will work for your enterprise.  This includes what you need to see, when you need to see it, and then mapping a path for the data back to the business-critical and, typically, legacy systems.  Data integration should be first and foremost when planning the strategy, technology, and deployments.

FacebookTwitterLinkedInEmailPrintShare
Posted in Data Aggregation, Data Integration, Data Integration Platform, Data Quality | Tagged , , , | Leave a comment

8 Information Management Challenges for UDI Compliance

“My team spends far too much time pulling together medical device data that’s scattered across different systems and reconciling it in spreadsheets to create compliance reports.” This quotation from a regulatory affairs leader at a medical device manufacturer highlights the impact of poorly managed medical device data on compliance reporting, such as the reports needed for the FDA’s Universal Device Identification (UDI) regulation. In fact, an overreliance on manual, time-consuming processes brings an increased risk of human error in UDI compliance reports.

frustrated_man_computer

Is your compliance team manually reconciling data for UDI compliance reports?

If you are an information management leader working for a medical device manufacturer, and your compliance team needs quick and easy access to medical device data for UDI compliance reporting, I have five questions for you:

1) How many Class III and Class II devices do you have?
2) How many systems or reporting data stores contain data about these medical devices?
3) How much time do employees spend manually fixing data errors before the data can be used for reporting?
4) How do you plan to manage medical device data so the compliance team can quickly and easily produce accurate reports for UDI Compliance?
5) How do you plan to help the compliance team manage the multi-step submission process?

Watch this on-demand webinar "3 EIM Best Practices for UDI Compliance"

Watch this on-demand webinar “3 EIM Best Practices for UDI Compliance”

For some helpful advice from data management experts, watch this on-demand webinar “3 Enterprise Information Management (EIM) Best Practices for UDI Compliance.”

The deadline to submit the first UDI compliance report to the FDA for Class III devices is September 24, 2014. But, the medical device data needed to produce the report is typically scattered among different internal systems, such as Enterprise Resource Planning (ERP) e.g. SAP and JD Edwards, Product Lifecycle Management (PLM), Manufacturing Execution Systems (MES) and external 3rd party device identifiers.

The traditional approach for dealing with poorly managed data is the compliance team burns the midnight oil to bring together and then manually reconcile all the medical device data in a spreadsheet. And, they have to do this each and every time a compliance report is due. The good news is your compliance team doesn’t have to.

Many medical device manufacturers are are leveraging their existing data governance programs, supported by a combination of data integration, data quality and master data management (MDM) technology to eliminate the need for manual data reconciliation. They are centralizing their medical device data management, so they have a single source of trusted medical device data for UDI compliance reporting as well as other compliance and revenue generating initiatives.

Get UDI data management advice from data experts Kelle O'Neal, Managing Partner at First San Francisco Partners and Bryan Balding, MDM Specialist at Informatica
Get UDI data management advice from data experts Kelle O’Neal, Managing Partner at First San Francisco Partners and Bryan Balding, MDM Specialist at Informatica

During this this on-demand webinar, Kelle O’Neal, Managing Partner at First San Francisco Partners, covers the eight information management challenges for UDI compliance as well as best practices for medical device data management.

Bryan Balding, MDM Solution Specialist at Informatica, shows you how to apply these best practices with the Informatica UDI Compliance Solution.

You’ll learn how to automate the process of capturing, managing and sharing medical device data to make it quicker and easier to create the reports needed for UDI compliance on ongoing basis.

 

 

20 Questions & Answers about Complying with the FDA Requirement for Unique Device Identification (UDI)

20 Questions & Answers about Complying with the FDA Requirement
for Unique Device Identification (UDI)

Also, we just published a joint whitepaper with First San Francisco Partners, Information Management FAQ for UDI: 20 Questions & Answers about Complying with the FDA Requirement for Unique Device Identification (UDI). Get answers to questions such as:

What is needed to support an EIM strategy for UDI compliance?
What role does data governance play in UDI compliance?
What are the components of a successful data governance program?
Why should I centralize my business-critical medical device data?
What does the architecture of a UDI compliance solution look like?

I invite you to download the UDI compliance FAQ now and share your feedback in the comments section below.

FacebookTwitterLinkedInEmailPrintShare
Posted in Data Governance, Data Integration, Data Quality, Enterprise Data Management, Life Sciences, Manufacturing, Master Data Management, Vertical | Tagged , , , , , , , , , , , , , , | Leave a comment

Reflections Of A Former Data Analyst (Part 2) – Changing The Game For Data Plumbing

 

Elephant cleansing

Cleaning. Sometimes is challenging!

In my last blog I promised I would report back my experience on using Informatica Data Quality, a software tool that helps automate the hectic, tedious data plumbing task, a task that routinely consumes more than 80% of the analyst time. Today, I am happy to share what I’ve learned in the past couple of months.

But first, let me confess something. The reason it took me so long to get here was that I was dreaded by trying the software.  Never a savvy computer programmer, I was convinced that I would not be technical enough to master the tool and it would turn into a lengthy learning experience. The mental barrier dragged me down for a couple of months and I finally bit the bullet and got my hands on the software. I am happy to report that my fear  was truly unnecessary –  It took me one half day to get a good handle on most features in the Analyst Tool, a component  of the Data Quality designed for analyst and business users,   then I spent 3 days trying to figure out how to maneuver the Developer Tool, another key piece of the Data Quality offering mostly used by – you guessed it, developers and technical users.  I have to admit that I am no master of the Developer Tool after 3 days of wrestling with it, but, I got the basics and more importantly, my hands-on interaction with the entire software helped me understand the logic behind the overall design, and see for myself  how analyst and business user can easily collaborate with their IT counterpart within our Data Quality environment.

To break it all down, first comes to Profiling. As analyst we understand too well the importance of profiling as it provides an anatomy of the raw data we collected. In many cases, it is a must have first step in data preparation (especially when our  raw data came from different places and can also carry different formats).  A heavy user of Excel, I used to rely on all the tricks available in the spreadsheet to gain visibility of my data. I would filter, sort, build pivot table, make charts to learn what’s in my raw data.  Depending on how many columns in my data set, it could take hours, sometimes days just to figure out whether the data I received was any good at all, and how good it was.

which one do you like better?

which one do you like better?

Switching to the Analyst Tool in Data Quality, learning my raw data becomes a task of a few clicks – maximum 6 if I am picky about how I want it to be done.  Basically I load my data, click on a couple of options, and let the software do the rest.  A few seconds later I am able to visualize the statistics of the data fields I choose to examine,  I can also measure the quality of the raw data by using Scorecard feature in the software. No more fiddling with spreadsheet and staring at busy rows and columns.  Take a look at the above screenshots and let me know your preference?

Once I decide that my raw data is adequate enough to use after the profiling, I still need to clean up the nonsense in it before performing any analysis work, otherwise  bad things can happen — we call it garbage in garbage out. Again, to clean and standardize my data, Excel came to rescue in the past.  I would play with different functions and learn new ones, write macro or simply do it by hand. It was tedious but worked if I worked on static data set. Problem however, was when I needed to incorporate new data sources in a different format, many of the previously built formula would break loose and become inapplicable. I would have to start all over again. Spreadsheet tricks simply don’t scale in those situation.

Rule Builder in Analyst Tool

Rule Builder in Analyst Tool

With Data Quality Analyst Tool, I can use the Rule Builder to create a set of logical rules in hierarchical manner based on my objectives,  and test those rules to see the immediate results. The nice thing is, those rules are not subject to data format, location, or size, so I can reuse them when the new data comes in.  Profiling can be done at any time so I can re-examine my data after applying the rules, as many times as I like. Once I am satisfied with the rules, they will be passed on to my peers in IT so they can create executable rules based on the logic I create and run them automatically in production. No more worrying about the difference in format, volume or other discrepancies in the data sets, all the complexity is taken care of by the software, and all I need to do is to build meaningful rules to transform the data to the appropriate condition so I can have good quality data to work with for my analysis.  Best part? I can do all of the above without hassling my IT – feeling empowered is awesome!

Changing The Game For Data Plumbing

Use the Right Tool for the Job

Use the right tool for the right job will improve our results, save us time, and make our jobs much more enjoyable. For me, no more Excel for data cleansing after trying our Data Quality software, because now I can get a more done in less time, and I am no longer stressed out by the lengthy process.

I encourage my analyst friends to try Informatica Data Quality, or at least the Analyst Tool in it.  If you are like me, feeling weary about the steep learning curve then fear no more. Besides, if Data Quality can cut down your data cleansing time by half (mind you our customers have reported higher numbers), how many more predictive models you can build, how much you will learn, and how much faster you can build your reports in Tableau, with more confidence?

FacebookTwitterLinkedInEmailPrintShare
Posted in Data Governance, Data Quality | Tagged , , , | Leave a comment