Can a small business act like a giant with SaaS?

It isn’t easy being a small fish in a big pond.  How does a small mortgage company leverage its ability to be nimble in a competitive market? I believe that SaaS offerings are initiating a revolution with broad implications for business models and competitiveness. It is becoming increasingly clear that with the advent of sophisticated software as a service (SaaS) environments combined with process as a service, it is possible and even commonplace for a small company to be competitive.  I had the opportunity to spend some time at IBM’s LotusSphere with a small mortgage company called Signature Mortgage based in Ohio.  By leveraging a combination of IBM’s LotusLive platform combined with Silanis Technology’s electronic signature platform, Signature Mortgage has been able to differentiate itself in an extremely complex market dominated by Fortune 500 companies.

Mortgage brokers like Signature Mortgage are involved primarily in the origination process of the loan, bringing mortgage borrowers together with mortgage lenders who actually fund the money for the loan. Mortgage borrowers engage with mortgage brokers early in the mortgage process when a consumer looking for a mortgage often has a lot of choices. It can be hard at this early stage for a consumer to differentiate between mortgage brokers and if one mortgage broker takes several days or longer deliver the mortgage application documents for signing, the consumer might switch to another broker. Typically a consumer selects a mortgage broker based on the offered rate, term, and closing costs and then locks down the rate to protect against a rate change.  The broker will facilitate a complex series of steps that must take place in order to move from this initial rate lock down to the approval and closing of the mortgage.  The consumer must submit financial and personal documentation so the mortgage broker can assess the credit worthiness of the individual and appraise the property.

The process between mortgage origination and closure can take as long as 45-60 days.  To make money, the mortgage broker needs to be able to collect all the required information quickly and then be in a position to close the deal with the mortgage lender before rates change.  There is a lot at stake for both the mortgage broker and applicant during this time period. For example, missing a deadline for a signature can lead to cancellation of locked-in rates on a mortgage commitment, potentially leading to higher costs for a buyer or lost revenue for a mortgage broker.

Bob Catlin, President of Signature Mortgage explained to me that he was determined to use his company’s small size as an advantage by quickly implementing innovative technology that might take much longer in a large company with legacy policies and infrastructure. By streamlining and speeding up the mortgage origination process, he could differentiate from the larger banks, increase profits and have happier more satisfied customers.

Signature Mortgage has its customers log in to a portal designed to capture best practices for submitting application documentation, revising documents, receiving status reports and securing electronic signatures when required. All of the steps in the mortgage process are documented within the Silanis Technology portal that is based on IBM’s LotusLive collaboration platform.  The Silanis electronic signature solution is delivered as a cloud-based service, making it attractive to a small business with a limited IT staff. By implementing Silanis Technology’s solution, Caitlin has been able to shave days off the loan origination process because there are no more last minute surprises resulting from missing data or documents and delays in arranging in-person meetings.

For Signature Mortgage, the first step in tightening the timeline for the mortgage process was to consistently lock in rates in less than 15 days. Caitlin is now working on decreasing application processing time to 24 hours and shortening the total time between mortgage application and closing down to 10-15 days. Any mortgage borrower who has dealt with requests for last minute faxes for missing documentation or errors that are discovered as the rate-lock deadline approaches will understand that decreasing the time to close from the industry average of 30-45 days is a big deal. However, speed isn’t the entire benefit; managing all information related to the mortgage applications in one centralized portal improves accuracy and accountability. Using the capabilities of LotusLive and Silanis e-signature, Signature Mortgage has been able to include features that make the online mortgage process intuitive and consistent with paper-based manual processes that are familiar to many people. For example, customers click to sign where they see a virtual sticky note similar to the process used at in-person signings. The consistency and repeatability of the process helps the company to maintain compliance with legal and regulatory requirements.

With this predictable foundation, Signature Mortgage has been able to grow quickly, increase profitability and build a strong presence in the community.

Asking the right questions about information governance

I am looking forward to attending The Smart Governance Forum (23^rd meeting of the IBM Data Governance Council) in California on February 1-3, where I will be a panelist for a session on Smart Governance Analytics. As my panel group started to plan for the event, I did some background research on the Council to understand more about them. What kinds of questions were Council members asking about information governance when they began meeting in 2004 and how are things different today? Have they developed best practices that would be useful to other companies working to develop an information governance strategy?

Information governance refers to the methods, policies, and technology that your business deploys to ensure the quality, completeness, and safety of its information. Your approach to information governance must align with the policies, standards, regulations, and laws that you are legally required to follow. When a group of senior executives responsible for information security, risk, and compliance at IBM customer organizations began meeting in 2004, interest in IT governance was high, but there wasn’t as much attention focused specifically on information governance.

Books like “IT Governance: How Top Performers Manage IT Decision Rights for Superior Results” by Peter Weill and Jeanne W Ross  helped companies understand the benefit of aligning IT goals with the overall goals and objectives of the business. In addition, there were other publications at this time focused on how to take a balanced scorecard approach to managing business strategy and on best practices for implementing IT governance.   These approaches are of critical importance to business success, however there was also a need to develop a framework for understanding, monitoring, and securing the rapidly increasing supply of business data and content.

And that is what a group of IT information focused business leaders and IBM and business partner technology leaders decided to do. The amount of data they needed to collect, aggregate, process, analyze, share, change, store, and retire was growing larger every day. In addition to data stored  in traditional data bases and packaged applications like CRM (customer relationship management) systems, they were also concerned about information stored and shared in unstructured formats like documents, spreadsheets, and email.

Having more information about your companies customers, partners, and products creates great opportunity, but more information also means more risk if  you don’t manage your information with care. Council members asked each other lots of questions such as:

• How can we be sure that the right people get access to the right information at the right time?
• How can we make sure that the wrong people do not get access to our private information at any time?
• How can we overcome the risks to data quality, consistency, and security increased by the siloed approach to business data ownership that is so prevalent in our organizations?
• How can we create a benchmarking tool for information governance that will help our businesses to increase revenue, lower costs, and reduce risks?
• How can improve our ability to meet the security and protection standards of auditors and regulators?

As a result of its discussions, The Council developed a Maturity Model to help you assess your current state of information governance and provide guidance for developing a roadmap for the future. The Model identifies 11 categories of information governance.  The categories cover all the different elements of building an information security strategy such as understanding who in the business/IT is responsible for  what information, what policies do you follow to control the flow of your information in your company,  what are your methodologies for identifying and mitigating risk,  and how do you measure the value of your data and the effectiveness of governance. I read two IBM White Paper’s on the Model that add insight to the questions you need to ask to begin building a path to better information governance,  “The IBM Data Governance Council Maturity Model: Building a roadmap for effective data governance” and  “The IBM data governance blueprint: Leveraging best practices and proven technologies“.

So, what’s changed? FInancial crises, increasing regulation, high-profile incidents of stolen private data, cloud technology, and other factors have added substance and complexity to the questions you need to ask about information governance.  There is much to do.  One question we will explore at the conference next week is, How do you measure the effectiveness of your information governance strategy and what analytical measures are appropriate? For example, some companies are using analytical tools to look for patterns of email communication across the company and discover a greater level of insight into how information is flowing and what needs more review. Look for more on analytics and governance after the conference.

Do you have an analytics strategy and why should you care?

After just returning from IBM’s Information on Demand (IOD) Conference in Las Vegas, I would like to take this opportunity to virtually whisper just one word in the ear of a current day Benjamin Braddock, “analytics”. Many businesses have spent the past 25 years or so automating and streamlining business processes in order to drive improvements in efficiency and productivity.  But now, it is becoming apparent that these businesses expect their future success will increasingly depend on how skillfully they manage, govern, and analyze information. Businesses are applying analytical techniques to business information to help reduce risk and increase the certainty that they are making the right decisions.

IBM has, in fact, spent $12 Billion in software investments (both organic multiple acquisitions like SPSS, Cognos, Filenet, iPhrase, and Ascential Software, just to name a few) over the past 4-5 years to ensure it will be able to support its customers in their quest to unlock the business value of information. In addition, in April of 2009 IBM announced a new organization comprised of 4000 consultants focused on advanced business analytics and business optimization – teams with skills in applying business intelligence technologies like mathematical modeling, simulation, data analytics, and optimization techniques.

In an era of intense competition, tight credit, and cost concerns across global and vertical markets, this focus on getting the most value from the information you have makes a lot of sense. Companies find they are processing more information than ever before, but less of this information is being accurately and adequately used.  The quantity of available data that a business needs to manage and understand has skyrocketed along with the increase in instrumented and intelligent products. For example, RFID tags that are embedded in manufactured products,  plants and animals generate an enormous amount of data in efforts to control inventories and improve security and safety.  Trying to make decisions with inadequate,  inaccurate, or untimely  information is like driving a fast sports car down the highway with a very large blind spot impeding your view of the truck approaching on your side. You need to know about the obstacles that might appear in  your pathway before you try to make a “real-time” correction and steer your car (or your business) of a cliff.  So, students and business leaders alike please take note, I see some “analytics” in your future.

Ten common mistakes companies make in data integration

One of the most critical IT management responsibilities is to ensure that the business has access to trusted information. This is actually a very challenging goal for many companies because the data needed to support business decision making is often inconsistent, redundant and of poor quality. Company data sources have become increasingly complex, often trapped in a complex tangle of disparate data stores and technology systems.

Large enterprises have typically approached the management of information in a siloed way. Each division or line of business within an organization such as finance, sales and marketing, operations, or a specific product line has been treated as a unique entity. Each entity requires different business applications and each of those applications has been tightly linked with its own data store. This siloed approach no longer meets the need of business users who need to understand and make decisions across the enterprise as a whole.

Why not? Each business application may need similar data, such as customer, product, or pricing data, but the definitions of these data may vary across departments. In addition, the data from the various data stores may have different structures, different interfaces, and even different semantics. The data on customers, products and services are often tied into a specific line of business. What happens when you want to cross sell across product lines. Are the definitions of the customer the same?

Creating a company wide environment of trusted information requires some data integration. This process requires a well-thought out architectural approach that will provide information about the business as a service to everyone who needs it. This architectural approach will typically require technology for ETL (extract-transform-load), quality management, the creation of a metadata layer, and a strategy for master data management (MDM).

Companies can often identify why data integration is required, but then fall short on implementing the technology in a way that maximizes the benefit to the business. It can be very challenging for companies to manage the data integration process successfully. Some of the biggest problems stem from a lack of understanding about the needs of the business. The process of data integration needs to be considered as part of an overall information management strategy for the business and within the context of the business strategy and priorities. It is important to consider the rules, strategies, and goals of the business as part of the process. Does this approach make sense to the business? Does this approach satisfy the requirements of the business?

If you follow a strictly technical approach to data integration you are likely to make some mistakes and fall short of reaching your desired goal. Successful companies look at information management holistically with an ultimate goal of providing trusted and consistent information about the business to everyone one from the CEO to customer service representatives to external partners and suppliers.

The following are ten common mistakes that should be avoided when planning for data integration.

1. Following a “fire-drill” approach to data integration. It is short sighted to use ETL technology as a tool to solve a one-time data integration problem rather than using this technology as part of a comprehensive approach to information management.
2. Not thinking about data as a shared and reusable resource. It is easier to budget based on getting a single task done. However, it is much more efficient and cost effective to be able to reuse data resources once the second, third and future projects are initiated.
3. Thinking tactically about data integration and missing out on opportunities to improve business process. Companies often implement data integration technology to eliminate time-consuming and labor-intensive processes that have been required to gain a consolidated view across business units. However, it is a mistake to focus on reducing head count and saving time in the data integration process, without also considering a broader strategic view towards improving overall business processes.
4. Not establishing an architectural framework with the capability of providing reusable information services. Once the data is decoupled from the business application, you need to develop a methodology that supports reuse so the data can be shared in different ways as needed. The information as a service approach is designed to ensure that business services are able to consume and deliver the data they need in a trusted, controlled, consistent, and flexible way across the enterprise.
5. Using software code to adjust for differences in definitions about customers, products, and other data types on a one-off project basis. In order to deliver information as a service, there needs to be repeatable way to manage complex processes without the expense and time required for recoding. This can be accomplished with the support of a metadata infrastructure.
6. Integrating data without placing a high priority on data quality. It is critically important that companies establish processes to cleanse and correct data as part of the overall data integration process. Creating standardized and consistent information will ensure that business users are more confident about business information and in a better position to grow the business and remain competitive.
7. Not creating a standardized way to handle data that is common to the various disparate IT systems and business groups. Companies need to understand the commonalities across different data types. This can best be achieved by developing a master data management (MDM) strategy to serve as the system of record for the consuming systems and applications.
8. The technical integration team and the business experts do not communicate effectively. There needs to be a shared and common language describing business processes to enhance communication between business and IT management. The business is more likely to have good quality information they can count on if the IT and the business establish an efficient process for sharing knowledge and requirements.
9. Business owners are reluctant to give up ownership of data. In order to gain the efficiency and accuracy in the data integration process, it is important to establish a consensus among the various data owners regarding data terminology and definitions, and there needs to be a clear understanding of the data lineage and who is responsible for these data over time. This often requires a significant cultural change because individual business experts often have a long history of managing data for their line of business or department as if it was a stand-alone entity. Companies need to find a way to balance the need for individual business experts to maintain control over their own data with the need for centralized management of data within a metadata environment.
10. Trying to do too much in one project. When data is integrated across departmental data silos, previously inaccessible data becomes available to business users. Companies can take on projects that would have been impossible before because of the enormous amounts of hand coding and manual data collection that would have been required. However, these benefits can be lost if companies try to tackle too much at once. Enterprise-wide information management projects must be approached in an incremental way so that there is time to evaluate and improve data quality, understand the needs of the business, and establish repeatable methodologies and processes.