Why quality data is critical — for your firm, your professionals, and your clients
Quality data is the basis for all meaningful, actionable insights — and without it, firms are likely to struggle with inefficient analysis, impaired decision making, and diminished trust in their organizational data and reporting. Inaccurate, duplicative information can create a variety of issues including risk exposure, increased complexity of firm data ecosystems, wasted time, and bogged-down processes that can ultimately lead to missed opportunities and lost revenue. In fact, according to Gartner, poor data quality costs organizations an average of $12.9 million every year.
For today’s firms operating in fast-paced, increasingly competitive markets, disjointed, poor-quality data is a problem they can’t afford. Yet many clients share that they feel frustrated and challenged by the task of tackling a data-cleaning initiative. Often, they’re eager to get started on a strategic project, and have made commitments to senior leadership regarding implementation timelines — then realized, as they’re about to get started, that there’s a huge amount of work and time involved in cleaning up historical data and setting standards and processes that support data integrity on an ongoing basis.
Across the professional and financial services verticals we serve, many of our clients share the same questions and concerns about why and how they should remediate their data. In this first post in a new data-focused blog series, I’ll break down the definitions and benefits of this critical foundational step in every digital transformation process.
First things first: What are the characteristics of quality data?
- Accuracy — The degree to which your firm’s data is free of erroneous information, redundancies, and typing errors.
- Completeness — The degree to which all necessary data is known and collected at the right point during a process in order to conduct the work needed to progress to the next step.
- Consistency — The degree of consistency within a given data set or across multiple data sets.
- Uniformity — The degree to which your firm’s data is specified using the same unit of measure.
- Validity — The degree to which your firm’s data adheres to defined business rules, governance, and controls.
What is data cleaning?
Data cleaning is the process of removing any data that doesn’t belong in your data set or add value in facilitating important internal processes such as client onboarding, risk checks, and billing set-up. Broadly speaking, it boils down to a few key steps:
- Removing duplicative, redundant, and irrelevant data to eliminate overcrowding and distraction, make your analyses more efficient, and reflect a clear understanding of the core data points needed to facilitate your firm’s internal processes (for example, by ensuring you do not have multiple same-client and/or work matter registrations in your systems).
- Fixing structural errors (such as typos and inconsistent naming conventions, address formats, abbreviations, and capitalization) to avoid mislabeled and inconsistent categories of data and inaccuracies when producing process outputs such as client correspondence or bills.
- Addressing missing data values, depending on the circumstances, by deleting the row(s) with missing values, imputing the missing data, or using regression or classification models to predict missing values.
- Reviewing and validating your data at the earliest opportunity to ensure its integrity, accuracy, and structure before using it for any business analysis, operation, or process.
How can we keep our data clean?
Once your data has been cleaned, your firm should consider how to uphold efforts to maintain quality data by eliminating, to the greatest degree possible, any variation and inaccuracy in entry.
Many firms continue to rely on manual, duplicative data entry methods by employees or via spreadsheet upload, which can lead to time wastage and custom entries. Some of these methods are of course acceptable; however, increased care needs to be applied to data quality input and review, which again can be more inefficient. Firms should instead consider how these labor-intensive data tasks can be reduced without sacrificing data quality, then implement processes and steps that include essential data checking on an ongoing basis.
Technology solutions that remove the need for manual entry — for example, by providing dropdown options for more precise, consistent data categorization — can be helpful. This consistency can help to ensure quality data input at the time of entry by eliminating any chance of misspellings, abbreviations, and terminology.
Then, taking it a step further, firms should wherever possible take advantage of system-to-system data integrations and automations — further reducing the need for manual entry (and human error) and supporting a more seamless data lifecycle. Where data is inaccurate or incomplete, there should be a built-in mechanism that allows the reviewer to confirm and fix the data at the earliest opportunity. Conducting these proactive checks at various steps during the client onboarding process reduces the likelihood of inaccurate data being passed to downstream teams for processing and makes for more reliable data for reporting purposes.
“Your data should tell a story. You want to be sure that story is accurate, and that you’re able to report it up correctly and with confidence.”
What are the benefits of cleaning data and improving data quality?
Firms, their professionals, and their clients typically enjoy a host of major advantages:
A sound data cleaning initiative tends to significantly refine and reduce the data points your firm collects, creating efficiencies for your clients, professionals, and teams.
Consider intake forms: They’re a critical source of data, and often facilitate most firm subprocesses in some way. However, it can be challenging to manage the volume of information they capture, with many forms requiring up to 500 (or more) data points. While there is no doubt that much data is needed to facilitate various firm processes, it’s important to take a step back and ensure that the information collected is absolutely necessary.
It’s helpful for client-facing and support teams to be educated on the data needed, and to understand why and how it is being used. For every data point needed, someone in your firm is likely responsible for key-in and review. There can be multiple back-and-forth communications in the event data input is inaccurate, so the need to take a right first-time approach can be beneficial, where all stakeholders involved take accountability.
Firms have an obligation to securely collect, store, and maintain high volumes of sensitive client information. Identifying, classifying, and documenting internal and external personal and corporate client information is critical to ensuring data privacy, governance, and other regulatory compliance. Whether firms are subject to regulations such as GDPR (General Data Privacy Regulations) or anti-money laundering (AML) compliance regulations, a robust approach should be taken to safeguard this data and assure clients that this is a priority for your firm.
Comprehensive data quality controls will also help your firm locate and resolve any personal data inaccuracies more quickly and easily. Maintaining and governing data appropriately can assure regulators and auditors that your firm takes a proactive and responsible approach towards complying with standards and meeting expectations regarding the handling of sensitive information.
Supporting business development and opportunities
Your data should tell a story. You want to be sure that story is accurate, and that you’re able to report it up correctly and with confidence. Large volumes of data are exchanged with your firm on a daily basis, and quality data can provide critical insights into the type of work your firm conducts. Some helpful questions to consider include:
- What types of clients and work does your firm onboard?
- Is this work commercially viable and aligning to your firm’s strategy and financial objectives?
- Is the client and work aligning to your firm’s risk profile and appetite?
By reporting and analyzing data of this nature, your firm and leadership will be better able to understand development opportunities — including how to better support existing clients with additional service offerings, where gaps exist in service offerings, and where and how performance can be enhanced to generate entirely new service lines.
Capturing meaningful data and reporting can also help affirm recognition for your firm’s work and achievements, which is appealing to prospective and existing clients. Rankings, awards, and recognition are increasingly important in a competitive market — and clients take note and keep up to date with them on social media.
Supporting client relations
Client relationship teams deal with a huge range of internal processes to support their clients’ demands. In an increasingly competitive environment, it’s important for firms to be clear with clients regarding the information needed and to progress through various internal processes swiftly and seamlessly to ensure a positive client experience. When your client teams have timely access to clean data, they can share accurate reporting with clients the first time around — sparing them additional effort, corrections, and time-consuming back and forth, and giving your clients a frictionless, personalized service that ensures that their needs and expectations are known and exceeded.
The bottom line
Ultimately, regular data hygiene and sound, ongoing data management and enrichment are essential to helping your firm keep its competitive edge in a constantly evolving environment.
In the next blog in the series, we’ll explore best practices for data governance at professional and financial services firms.
To learn more about maintaining and assuring accuracy and consistency over the data lifecycle, download our white paper.
Marie-Claire Le Houerou is the Head of Risk Solution and Project Strategy at Intapp. She previously served as the Global Director of New Business Intake at Baker McKenzie, where she was responsible for intake risk management and operations. Prior to this, she worked with State Street, initially in a client-facing capacity in private equity and real estate fund administration, before establishing an EMEA-based client onboarding function, where she was responsible for managing new business and the entire quote to cash process.