Hipercept

Project Success

in an investment environment that demands more information for less

By Damien Georges & Howard Cohen

hcohen@hipercept.com

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Improving data quality is at the forefront of most companies’ minds these days. While data quality is seen as improving through improved software, companies are still losing enormous amounts of money through bad data. A recent Gartner survey found the following:

• The 140 companies surveyed lose an average of $8.2 million annually through faulty data
• 30 of the companies surveyed estimated their losses at $20 million
• 6 companies estimated their losses to be as high as $100 million annually

Much of this loss, according to Gartner, is due to lost productivity among workers who — realizing their data is incorrect — are forced to compensate for the inaccuracies by creating workarounds.

In the private equity real estate sector, the issue of data quality is particularly acute. Important information such as tenant exposure analysis, occupancy reports and portfolio-wide lease expiration schedules are hard to come by and often manually prepared. Further, most information is still prepared on an annual basis, or at best a quarterly basis. With rapidly changing market conditions, these reports very quickly become outdated and irrelevant, leaving the company in the dark for a significant portion of the year. Much of the data is also unstructured which makes it virtually impossible to consolidate the information, slice and dice it for analysis purposes, or search across multiple reports.

The solution is people and process with technology as an enabler. There is a six step solution to improving data quality:

1. Define what data governance means

   A working definition of data governance is “The formal orchestration of people, processes and technology to enable an organization to leverage data as a strategic asset.”

2. Create a data governance program

   A data governance program should include a governing body (a steering committee or council), an agreed upon common set of procedures and a plan to communicate and execute those procedures. Improving data quality means that the right data is used by a person in the right role only in the right context.

3. Get past the Excel culture

   Many companies are locked into an Excel culture in which data is extracted from internal systems and loaded to spreadsheets. Calculations and analyses are then created that are not shared companywide. This results in multiple and oftentimes competing analyses that create confusion and even risk from unmanaged and unsecured data held locally on individual PCs.

   Business users must take a leadership role in any data governance initiative — only with their full engagement and commitment to culture change will an investment in data governance ever realize its full potential.

4. Create a Data Dictionary / Standards

   A data governance initiative aims to create a single version of the truth but many organizations have not even agreed on the definition of fundamentals such as “revenue”. Achieving one version of the truth requires cross-departmental agreement on how business entities (customers, products, key performance indicators, metrics, etc) are defined. Many organizations wind up creating siloed data governance initiatives that perpetuate the disparate definitions of their current systems.

   Organizations should start with their published information and metrics, such as annual or quarterly reports. These definitions should be put into a database and “published” internally so that all users are familiar with the data standards.

5. Make sure that when IT builds it, they will come

   All too often, the IT organization sponsors, funds and leads data governance initiatives. Project after project starts with the promise of massive benefit, sure term returns on investment and iron clad business cases. The danger in this approach is that the value is not obvious to the business and so all the hard work does not result in adoption by business users. This results in the perception by the business users that the project has failed. Although project methodologies and standardized processes can promote project success, the main problem is invariably the disconnect between business and technology resources.

6. Appoint a Data Governance Manager

   The Data Governance Manager is responsible for the implementation and oversight of the Company’s data management goals, standards, practices, process and technologies. This includes the Company’s Data Management System.

   The Data Management System includes the Company’s Data Integration System which consolidates and coordinates the Company’s data assets, any Data Framework applications needed to implement analysis, internal data sources and external data sources. Other data management tools, such as Business Intelligence applications, may also be part of the system.

   The Manager shall have the business experience to understand how data can be utilized within the Company, along with the ability to communicate with Company executives and staff to understand their data and information needs; provide guidance and direction on usage of the Data Management System; and develop analysis reports and key findings to Company staff.

   The Manager will have sufficient technical knowledge of data management and databases to help with the development and ongoing administration and management of the Data Management System.

   This is a cross functional position requiring coordination with various company departments. The position mission is to be empowered by direction from Executive Management as to the importance of effective data management and its use to enable the accomplishment of the Company’s strategic and tactical goals.

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