The Malartu Data Maturity Model
A data maturity model is a structure used to pinpoint where an organization currently stands on their path to becoming data driven. Data maturity models are helpful in outlining the steps required to extracting maximum value from the data available to your organization while communicating that vision effectively to your team.
To better communicate how we think about creating value through data within an investment group, we have developed the Malartu Data Maturity Model (MDMM). The goal of the MDMM is three-fold:
To provide a simple assessment tool for where your firm currently sits
To guide milestones for implementing the Malartu data platform
To avoid pitfalls in establishing data capabilities at your firm
The MDMM is derived from the Dell data maturity model and optimized for the private market investment firm. Resourceful firms like Dell apply analytics to understand correlations and connections within their portfolio operation, due diligence, or other reporting and arrive at better, faster, decisions.
To get to the point of being truly data driven, your firm needs to know where you’ve been and where you currently stand in your path to data maturity. It’s important to follow this model because trust is built along this path - it is impossible to make important strategic decisions based on data without trust that your data and systems are reliable.
The Malartu Data Maturity Model consists of four stages:
Data Aware: Your data is organized in one place and there is a system in place to continue this aggregation
Data Proficient: You have identified taxonomies and metadata to categorize data in meaningful ways
Data Intelligent: You can do more than than pinpoint historical trends, you can combine granular sources and begin to predict future outcomes
Data Driven: Your business revolves around data, you can predict future outcomes and prescribe strategic initiatives.
Stage 1: Becoming Data Aware
Becoming data aware is the first step to harnessing the power of your data. Before the first stage of the MDMM, an employee of the firm likely spends more time looking for a piece of information than analyzing it. The hunt that ensues while searching for information wastes your employees time, your time, and inherently your ability to make critical decisions that can benefit the firm. There is minimal trust in the reports available to firms that have not achieved data awareness because of systematic flaws that favor human error and data inconsistencies.
Partners in stage one of the data maturity model often find themselves asking associates or analysts questions like, "What was revenue in Q4 of last year for portfolio company X?" While this information is probably attainable, it may take hours to arrive at the answer.
To move forward and become data aware, two goals must be achieved.
Establish a central location to organize existing data
Identify the data sources relevant to portfolio operators, both internally and externally.
The Malartu Document Reader turns any data table within an Excel file, PDF, PNG, or JPG into workable data within the Malartu system (or a csv file with our free tool). Financial statements, quarterly reports, budgets, and pitch decks can be securely sent to Malartu’s cloud platform, where the data is available to your team, securely, anywhere in the world.
Becoming data mature is not just a technological shift, but also a cultural shift. In stage one of the maturity model, it it is essential to review governance structures in place that confirm your firm is getting the metrics needed from each company to make basic strategic decisions.
The Malartu Reader
Harness the power of Malartu and Google Cloud Vision to transform any data table within a PDF, PNG, or JPG into a workable CSV table.
Step 2: Becoming Data Proficient
Firms at stage two identify taxonomies and metadata that help categorize and organize data in meaningful ways. Furthering the cultural shift necessary for achieving data maturity, stage two firms begin building trust in their data systems by understanding where data is coming from, how metrics are calculated, and how to find answers to strategic questions efficiently.
Firms leveraging Malartu become data proficient by organizing their portfolio and aggregate data in custom dashboards and reports. Dashboards consist of custom calculations and visualizations that can include anything from financial ratios to custom models developed by a firm.
The ability to quickly toggle visualizations and data display periods is particularly important to becoming data proficient because it allows for on-demand analysis. For example, the ability to add context to a weekly meeting by quickly comparing Company X’ budget vs actual for last quarter or chart a revenue comparison to that of a similar portfolio company over the last three years.
Stage 3: Becoming Data Intelligent
At this stage your data is organized, accessible by decision makers, and you have the ability to quickly answer strategic decisions based on historical data.
Data intelligence requires going beyond historical analysis and moving toward predictive analysis. At this stage predicting market growth and customer demand becomes easier because we have a thorough understanding of what has lead to this demand in the past.
With historical data aggregated and organized, stage 3 funds begin to ask themselves what is missing to make the most informed decision. Based on this company’s operation, what do we need to dive into deeper to make the best decisions? Data from a CRM to measure sales efficiency? ESG-related initiatives to make sure we’re being effective? Data from the ERP system to manage inventory costs?
Firms who are on their way to being data intelligent leverage Malartu to connect directly to data sources so that they may automate reporting and capture data at a granular level. To inform even the simplest predictive models, a firm needs enough data points to achieve real significance. Often this involves more data aggregation than what is cost effective for analysts to do manually. The Malartu data platform leverages native API integrations to pull granular historical data sets from almost any system and continue to automate this reporting in the future.
With this granular data being piped into Malartu, a firm can begin to build out more complex models that begin to leverage predictive analytics, whether through in-house models or newer, more powerful machine learning algorithms.
Stage 4: Becoming Data Driven
Firms that are data driven make no decision without data. Their entire model revolves around data being freely available, trusted, and relevant. At stage four, firms have developed a system to aggregate, organize, and analyze a plentiful amount of data and can begin thinking about how they might use this data to achieve prescriptive analysis.
While the most difficult analysis to achieve, prescriptive analytics in private market investing can be described as more quickly arriving at a strategic initiative based on what you think will happen in the future. Inherently, a firm cannot reach stage 4 or produce prescriptive analytics without first establishing descriptive analytics (stage 1 and 2) and predictive analytics (stage 3). Prescriptive analysis takes your firm further by not only describing what might happen, but actions you can take to benefit from these changes, and what might happen from making changes.
An example of how Malartu can help your first become data driven and begin to think about prescriptive analytics is to leverage Malartu machine learning partnerships to analyze data you have aggregated and organized through the Malartu platform to arrive at prescriptions like, “Based on comparing data uncovered in due-diligence to the performance of our portfolio, we should pass on this company” or “Company A should increase R&D spend relative to what we’ve seen in top-performing company data sets.”
While no model is infallible, achieving stage four data maturity is an immense competitive advantage to other firms, and one we are excited to help our customers realize.
When thinking about a data platform for your fund there are a number of important factors to consider including aggregation, visualization, security, and interoperability. Here are a few factors that put Malartu in a league of our own:
A true Technological Advantage
Unlike other programs, our platform doesn’t rely on humans to enter data manually on behalf of your firm. Shifting data aggregation from your team of analysts to an outsourced team of analysts doesn’t effectively mitigate the risk of human error, so we have built a true technological advantage for data aggregation by leveraging deep learning and machine vision to automate data transposition and data entry.
Some competing platforms aggregate data by creating standard financial reports that are implemented at each portfolio company. This process passes the burden of your firm’s data aggregation down to portfolio companies. If you have ever had this conversation with a controller or CFO at a top-performing portfolio company, you know that this is not an easy obstacle to overcome. Additionally, this approach does not bring efficiency to aggregation which prevents a user from enriching existing reports with supporting data.
Our document parsing technology can read, extract, and organize the data found in your portfolio’s existing reports so that portfolio executives can continue running their business how they see fit. This approach allows us to quickly aggregate historical data and implement a more sophisticated level of data aggregation into more areas of your operation like due-diligence, creating a massive proprietary data set for your firm to leverage for comps, benchmarking, and predictive analytics.
Data Aggregation and Reporting Flexibility
Every team has a nuanced way of viewing data, there is no standard portfolio review across two different operations teams and there are typically unique key metrics to different industry focuses. Malartu’s data aggregation process and bespoke visualization features allow firms to completely customize the data they aggregate from portfolio company to portfolio company as well as the way they report at an aggregate fund level.
This reporting flexibility promotes the aggregation and reporting of non-financial data like sales, operations, and ESG data. Aggregating and analyzing this data is imperative to becoming data driven since this data tells the story behind financial outcomes.
Achieving Data Maturity
We didn’t just build Malartu to improve on your current processes, we built our platform to transform our customers into data driven value creators. Financial services and technology is often an antiquated business, many platforms stop at data awareness. Between our superior aggregation and reporting capabilities to our more advanced technical partnerships, Malartu is the only platform that can truly take you from where you are today to where you want to be as a data driven organization.
Contact our team to learn more
Becoming a data driven organization is a process. Contact our team to discuss how you can leverage the Malartu data platform and Malartu Data Maturity Model to bring your organization to the next level.