“Everything that informs us of something useful that we didn’t already know is a potential signal. If it matters and deserves a response, its potential is actualized.” – Stephen Few
A solution that provides a platform for intelligent decision-making in the enterprise is not about the technology, but the information and insight the solution delivers.
The vast array of new technology solutions and their associated data architectures that have appeared in the BI/analytics and big data landscape recently have been well documented.
From traditional dimensional BI architectures to advanced analytic and big data solutions, organizations now have a number of different approaches available for creating intelligent enterprise information. To add to this, the volume, variety and velocity (the 3Vs) of data present organizations with both an opportunity but also architectural challenges for their solutions.
Whether the organization opts for a more traditional relational BI model or a solution that incorporates relational and non-relational types the challenge of poor quality data still persists. This problem is often the by-product of a lack of any data quality management process. Data quality management should be an ongoing effort within an organization and not just a one-time exercise around a BI/analytics project.
There can often be the perception that the quality of data is someone else’s responsibility in the organization, and more often than not this view breeds a distinct lack of interest in the value of data and subsequently its quality in general.
Leadership from key stakeholders is essential if the organization is to create a culture that recognizes the value of data and its potential to deliver insight and opportunity. To create a collective focus on data quality, the stakeholder’s energy, enthusiasm and buy in to the process has to be clear for everyone to see. Real stakeholder leadership in data quality initiatives can have a transformative effect on the wider organizational community and a similar effect on the eventual quality of the data itself.
To enable change in the organization a number of key questions around data need to be posed. These same questions can become the basis for some ongoing high level KPIs/metrics that the organization can use to embark on a data quality improvement process:
- Completeness. What data is missing, incomplete or unusable?
- Consistency. Do data values provide conflicting information?
- Accuracy. What data is out of date or incorrect?
- Conformity. What data is stored in a non-standard format?
- Duplication. Are records being duplicated. If so, how, where and why?
- Integrity. What data is not referenced or just missing completely?
A lack of any of these key data quality characteristics has the potential to cost the organization in a number of areas:
- Incorrect client billing data resulting in delayed settlement of invoices.
- Operational and management reporting differences.
- Increased workloads and processing time to correct recurring data anomalies and inaccuracies. Potential to impact client service down the line.
3. Client confidence and satisfaction
- Reporting delivering differing numbers against billing, revenue and CRM data.
- Key client contact information differing between source systems.
- Prospect details incomplete or inaccurate resulting in missed opportunities/new business.
4. Risk and compliance
- At the outset of the client engagement, are we correctly capturing the core data attributes of our clients so that we can accurately assess them from a conflicts and risk perspective?
- This core data will then form the basis for a large percentage of our BI/analytics reporting content.
It is clear that the management and development of a data quality program does not start with a BI/analytics project – the BI/analytics project is just a component in the data quality management landscape. The analytics solution may be a far more visible component but is as liable to the effects of poor data quality outcomes as any area of the organization. A considerable chunk of content from the analytics solution will be presented externally to the organization’s clients.
An experienced data conversion team can provide not just expertise around the conversion process but also advice and guidance around ongoing internal day-to-day data quality procedures.
The realization at the beginning of a data conversion that a considerable data profiling and cleansing exercise should have been carried out has the potential for project deadlines to be missed.
The lack of a policy in this area I have discussed in the points above, but the benefits are clear in these areas of the organization also:
- The timely delivery of accurate client reporting.
- The ability to target business development campaigns based on high quality prospect data.
- The monthly client billing documents despatched with accuracy and timeliness, using such sites like https://fastspring.com/subscription-management/recurring-billing/ to provide a base.
- Operational finance reconciliations quickly and efficiently resolved.
- The client/matter lifecycle data captured accurately and consistently across CRM, Client Matter Inception and ERP/PMS repositories.
- Better cross functional collaboration through trusted, accurate data assets throughout the organization.
How can Helm360 Help?
Helm360’s extensive experience of data conversion include delivery of over 30 Elite Enterprise to Elite 3E conversions. This experience has allowed us to fully appreciate the challenges of moving from a legacy PMS to a new system and allowed us to build skills, tools and knowledge to make this process as pain free as possible. Our hybrid delivery model for data conversion tasks allows us to deliver services at a low cost to our customers in the following areas:
- Pre-Conversion data cleansing routines
- Data quality process design
- Data migration design and execution
In addition to the above, Helm360 offers an Elite Enterprise pre-conversion readiness checking service which consists of:
- Terminus EnSight Business Intelligence solution – with out of the box data integrity dashboards to allow customers to visually assess the quality of data in their Elite Enterprise system. Terminus EnSight is also a comprehensive Business Intelligence solution which connectors for Elite Enterprise, ProLaw and 3E.
- Pre-implementation environment check – a service to fully understand your Elite Enterprise system including:
- Identification of custom programs (4GL and VBA) so that you know what custom solutions your business is dependent on today
- Review of current database usage and any potential clean up areas
- Review of customized tables/columns that may have been changed from stock
- Review of system activity to determine which parts of the PMS are currently in use
- Review of any 3rd party integrations that may be interacting with your PMS
A robust data quality process is fundamental to a legal organization at a time when the pressure to deliver legal services more efficiently and at a lower cost is clear. Data quality should play a key part in enabling the delivery of these services.