Tag Archives: Data Quality
EMR vendors have long encouraged that their EMR and business intelligence capabilities negate the need to have a plan to integrate data or implement a separate data warehousing and business intelligence. This philosophy begs the question – how can one transactional clinical application support the intelligence needs of an enterprise? Consider customer relationship management data for feeding customer driven marketing initiatives, time tracking data full of valuable employee utilization stats, payer claims data and newly acquired practices running an EMR independent of Epic… just to name a few.
With the recognition that an EMR accounts for only a fraction of the data needed for reliable and comprehensive business intelligence comes requirements to reconcile terminology and data quality standards across an increasingly large set of trading partners and stakeholders, to access data from other sources (like payroll, CRM and claims) and to migrate clinical data from legacy applications.
In fact, business intelligence and analytics are dependent on data from across the enterprise. Most clinical and financial decisions are dependent on data; great potential lies within data – making it a valuable asset. This is not a new idea. What is a newer concept is what it means to really elevate data to the status of an asset. Unlocking the potential of data as an asset requires that healthcare organizations begin to think about and invest in data in new ways; making investments beyond traditional infrastructure like databases and data storage. Healthcare organizations must make investments in the ongoing management and improvement of the data itself as they do with any other asset, like talent, buildings or their EMR – for example understanding its quality and allocating people and systems to managing it. Moving faster in this competitive climate and delivering differentiated results requires it.
Check back next week for Part II which explores treating data as an asset further.
In my recent blog posts, we have looked at ways that master data management can become an integral component to the enterprise architecture, and I would be remiss if I did not look at how MDM dovetails with an emerging data management imperative: big data and big data analytics. Fortunately, the value of identity resolution and MDM has the potential for both contributing to performance improvement while enabling efficient entity extraction and recognition. (more…)
Following up on the discussion I started on GovernYourData.com (thanks to all who provided great feedback), here’s my full proposal on this topic:
We all know about the “Garbage In/Garbage Out” reality that data quality and data governance practitioners have been fighting against for decades. If you don’t trust data when it’s initially captured, how can you trust it when it’s time to consume or analyze it? But I’m also looking at the tougher problem of data degradation. The data comes into your environment just fine, but any number of actions, events – or inactions – turns that “good” data “bad”.
So far I’ve been able to hypothesize eight root causes of data degradation. I’d really love your feedback on both the validity and completeness of these categories. I’ve used similar examples across a number of these to simplify. (more…)
Since I joined Informatica over a year ago, I’ve received a daily stream of unsolicited emails from vendors selling “marketable user email/contact list databases” of myriad software and hardware technologies ranging from enterprise apps, business intelligence, Cloud computing, networking and infrastructure, etc. You get the idea – and I’m sure many of you experience a similar phenomenon on a daily basis.
My catalyst for writing a post about this is when I considered the relevance, transparency and quality requirements that data governance leaders strive for –and how these vendors seem to dismiss all of the above. (more…)
In my last blog post I discussed why an understanding of corporate financial concepts is so important to data quality success. In this blog, I will examine knowledge of commercial enterprise applications as a key enabler of effective data quality delivery.
Packaged applications for ERP, CRM, MRP, HCM, etc. were first introduced decades ago to provide tightly integrated business management functions, standardized processes and streamlined transaction processing. While one can argue whether or not these applications have lived up to all of the hyperbole, the reality is that they have been successful and are here to stay. As these backbone systems continued to evolve and mature, lessons learned from thousands of implementations were incorporated into the model solutions as best practices. These best practices spawned industry standard processes and specialized variants were born (e.g. vertical systems solutions). With the widespread adoption of these solutions, the days of custom building an application to meet the business’s needs have largely disappeared (although exceptions do persist to support specialized needs). (more…)
In my recent white paper, “Holistic Data Governance: A Framework for Competitive Advantage”, I aspirationally state that data governance should be managed as a self-sustaining business function no different than Finance. With this in mind, last year I chased down Earl Fry, Informatica’s Chief Financial Officer, and asked him how his team helps our company prioritize investments and resources. Earl suggested I speak with the head of our enterprise risk management group … and I left inspired! I was shown a portfolio management-style approach to prioritizing risk management investment. It used an easy to understand, business executive-friendly visualization “heat map” dashboard that aggregates and summarizes the multiple dimensions we use to model risk . I asked myself: if an extremely mature and universally relevant business function like Finance manages its business this way, can’t the emerging discipline of data governance learn from it? Here’s what I’ve developed… (more…)
The need to be more customer-centric in financial services is more important than ever as banks and insurance companies look for ways to reduce churn as those in the industry know that loyal customers spend more on higher margin products and are likely to refer additional customers. Bankers and insurers who understand this, and get this right, are in a better position to maintain profitable and lasting customer loyalty and reap significant financial rewards. The current market conditions remain significant and will be difficult to overcome without the right information management architecture to help companies be truly customer centric. Here’s why:
- Customer satisfaction with retail banks has decreased for four consecutive years, with particularly low scores in customer service. Thirty-seven percent of customers who switched primary relationships cited in an industry survey showed poor customer service as the main reasons.
- The commoditization of traditional banking and insurance products has rapidly increased client attrition and decreased acquisition rates. Industry reports estimate that banks are losing customers at an average rate of 12.5% per year, while average acquisition rates are at 13.5%, making acquisitions nearly a zero-sum game. Further, the cost of acquiring new customers is estimated at five times the rate of retaining existing ones.
- Switching is easier than ever before. Customer churn is at an all-time high in most European countries. According to an industry survey, 42 percent of German banking customers had been with their main bank for less than a year. As customer acquisition costs running between of €200 to €400, bankers and insurers need to keep their clients at least 5 to 7 years to simply break even.
- Mergers and acquisitions impact even further the complexity and risks of maintaining customer relationships. According to a recent study, 17 percent of respondents who had gone through a merger or acquisition had switched at least one of their accounts to another institution after their bank was acquired, while an additional 31 percent said they were at least somewhat likely to switch over the next year.
Financial services professionals have long recognized the need to manage customer relationships vs. account relationships by shifting away from a product-centric culture toward a customer-centric model to maintain client loyalty and grow their bottom lines organically. Here are some reasons why:
- A 5% increase in customer retention can increase profitability by 35% in banking, 50% in brokerage, and 125% in the consumer credit card market.
- Banks can add more than $1 million to the profitability of their commercial banking business line by simply extending 16 of these large corporate relationships by one year, or by saving two such clients from defecting. In the insurance sector, a one percent increase in customer retention results in $1M in revenue.
- The average company has between a 60% and 70% probability of success selling more services to a current customer, a 20% to 40% probability of selling to a former customer, and a 5% to 20% probability of making a sale to a prospect.
- Up to 66% of current users of financial institutions’ social media sites engage in receiving information about financial services, 32% use it to retrieve information about offers or promotions and 30% to conduct customer service related activities.
So what does it take to become more Customer-centric?
Companies who have successful customer centric business models share similar cultures of placing the customer first, people who are willing to go that extra mile, business processes designed with the customer’s needs in mind, product and marketing strategy that is designed to meet a customer’s needs, and technology solutions that helps access and deliver trusted, timely, and comprehensive information and intelligence across the business. These technologies include
Why is data integration important? Customer centricity begins with the ability to access and integrate your data regardless of format, source system, structure, volume, latency, from any location including the cloud and social media sites. The data business needs originates from many different systems across the organization and outside including new Software as a Service solutions and cloud based technologies. Traditional hand coded methods and one off tools and open source data integration tools are not able to scale and perform to effectively and efficiently access, manage, and deliver the right data to the systems and applications in the front lined. A the same time, we live in the Big Data era with increasing transaction volumes, new channel adoption including mobile devices and social media combined generating petabytes of data of which to support a capable and sustainable customer centric business model, requires technology that can handle this complexity, scale with the business, while reducing costs and improving productivity.
Data quality issues must be dealt with proactively and managed by both business and technology stakeholders. Though technology itself cannot prevent all data quality errors from happening, it is a critical part of your customer information management process to ensure any issues that exist are identified and dealt with in an expeditious manner. Specifically, a Data Quality solution that can help detect data quality errors in any source, allow business users to define data quality rules, support seamless consumption of those rules by developers to execute, dashboards and reports for business stakeholders, and ongoing quality monitoring to deal with time and business sensitive exceptions. Data quality management can only scale and deliver value if an organization believes and manages data as an asset. It also helps to have a data governance framework consisting of processes, policies, standards, and people from business and IT working together in the process.
Lastly, growing your business, improving wallet share, retaining profitable relationships, and lowering the cost of managing customer relationships requires a single, trusted, holistic, and authoritative source of customer information. Managing customer information has historically been in applications across traditional business silos that lacked any common processes to reconcile duplicate and conflicting information across business systems. Master Data Management solutions are purposely designed to help breakdown the traditional application and business silos and helps deliver that single view of the truth for all systems to benefit. Master Data Management allows banks and insurance companies to access, identity unique customer entities, relate accounts to each customer, and extend that relationship view across other customers and employees including relationship bankers, financial advisors, to existing agents and brokers.
The need to attract and retain customers is a continuous journey for the financial industry however that need is greater than ever before. The foundation for successful customer centricity requires technology that can help access and deliver trusted, timely, consistent, and comprehensive customer information and insight across all channels and avoid the mistakes of the past, allow you to stay ahead of your competition, and maximize value for your shareholders.
 2010 UK Retail Banking Satisfaction Study, J.D. Power and Associates, October 2010.
 “Customer Winback”
 Mortgage Servicing News
This year marks the 20th anniversary for Informatica. Twenty years of solving the problem of getting data from point A to point B, improving its quality, establishing a single view and managing it over its life-cycle. Yet after 20 years of innovation and leadership in the data integration market, when one would think the problem had been solved, all data had been extracted, transformed, cleansed and managed, it actually hasn’t — companies still need data integration. Why? Data is complicated business. And with data increasingly becoming central to business survival, organizations are constantly looking for ways to unlock new sources of it, use it as an unforeseen source of insight and do it all with greater agility and at lower cost. (more…)
No organization begins to implement a data governance program from an entirely blank slate; every organization likely has some capabilities to leverage. Determining an organization’s current level of data governance maturity is a useful and necessary first step in developing a customized plan that is both relevant and executable. So how do you assess your maturity? Well throw a rock in any direction and you’re likely to hit a software vendor, consulting company or industry analyst that offers a maturity model and assessment tool to support your data management and data governance efforts. Actually don’t throw rocks, you could hurt somebody. (Yes, we offer one too – more on that below). (more…)