Free Guide:

Data & Tech Guide
for Private Equity Firms

    Formulating a Data Strategy

    In the E&Y Private Equity survey, respondents reported spending more time on technology as their second highest focus behind analytics, which arguably could also fall under the technology umbrella.

    While it’s tempting to skip right to demos of the exciting capabilities of web-based data visualization and reporting products, a lack of dashboards isn’t necessarily the issue.

    In advance of seeing any technology platform, you should consider what problems or gaps you are looking to solve with your future-technology partner.

    Having identified the specific problems you’re looking to solve, you’re one step closer to getting demos, but still not ready. Understanding what a solution to these problems includes and the basics of how it will work is key to making the most of the time you spend getting demos.

    In this guide, we review next steps for firms to take when implementing a new technology initative to ensure their data transfers seamlessly.

    The value of deriving insights from your data is inevitably distinct for every firm, but on a basic level, the process — irrespective of the tools involved — is made up of the following steps:

    1. Accessing and/or connecting to data

    2. Preparing data to be properly analyzed

    3. Performing the analysis on and/or consuming the data

    This may seem straightforward enough but consider each of these steps against the problems you’ve identified and the gaps in your current situation. Consider specific examples of data-related workflows such as fulfilling information requests, preparing diligence materials, reporting to partners, generating investor-facing materials, answering ad-hoc questions, etc.

    Ask the following questions to determine the direct or indirect cost of your current processes:
    • Where are we dependent upon people for specific and/or costly skills?
    • Who is involved? How much time do they spend? Is this the best use of their time?
    • Where is the risk? How error-prone is this? How do we know whether it’s right or wrong?
    • Who benefits? How and why?
    • What would we need to improve this? Who would need to be involved, and what do we stand to gain?

    If you’re like most, you’ll discover that this simple process, even on a small scale is littered with potential problems that you’ll want to consider in the development of your technology strategy. For a successful firm-wide data and technology strategy, you must be clear on what you’re trying to accomplish, what are the current problems you need to solve, and where these problems occur.

    Approaching the Process

    Once you have a clear understanding of the gaps your firm faces, you’ll have a better idea of what to look for from providers. There are countless combinations of products and/or processes that this basic 3 step procedure uses to get the answers your firm needs.

    Step 1:Accessing and/or Connecting to Data

    The first step in any analytics value chain is first accessing the data, in its current form, and having an idea of its ideal form. If you know that your data is in flat files (like Excel) then you will need to assess how many spreadsheets, contacts and reports you need categorized versus if your data is already in a database, your work at hand is much less. Each of these approaches has its own advantages and disadvantages in the consideration of a data strategy.

    Step 2:Preparing Data to be Properly Analyzed

    The preparation step is arguably the most important, as proper preparation will make or break whether the analysis can be accurately and easily performed and subsequently consumed. No matter its ultimate source, all data must be prepared and that can mean doing something as simple as using Excel to correct typos or deduplicate rows.

    The capabilities of modern data preparation tools, on the other hand, have evolved from Excel’s shortcomings; the limited, simpler approaches that revolve around Excel can be extremely time-consuming and extremely prone for human error. They aren’t predictable nor repeatable and can often lead to analytical errors that aren’t always apparent in consumption.

    Modern data solutions prepare data in ways that are far more sophisticated, using powerful data warehouses to store real time snapshots of disparate data sets in one single source-of-truth, perform persistent transformations of the data sets such that they match structure and syntax, and automatically deduplicate and prepare data on-the-fly for analysis and consumption downstream. Once configured, this approach persists and perpetually provides analysis that eliminates human intervention and the risks that come with error-prone processes and tools with limited capabilities.

    The preparation layer is likely to be one, in identifying problems with your data, that uncovers a centralized dependency on specific skills and/or long periods of time required to meet reporting demands. In assessing their current situation against this step, many firms become aware of an unhealthy dependency upon analysts with impressive Excel skills. Then, upon seeing modern capabilities, discover that the time required of analysts to perform these analyses — together with the risky error-prone nature of them doing so — is a compelling reason to look at turning to technology to improve this step. In doing so, analysts become valuable resources that can be redeployed to other more valuable activities — in many cases ones nobody had been doing — that allow firms to become strategic in differentiating themselves.

    Step 3:Performing the Analysis on and/or Consuming the Data

    Properly prepared, data is now ready for its ultimate purpose to be analyzed and consumed. This is the area that modern technology has dramatically changed the ways in which firms visualize their data. The scale at which data can be interacted with, and even the influence of Artificial Intelligence technology to alert and predict insights based upon data without humans even having to ask the questions.

    One of the biggest modern innovations in this regard comes from tools that provide business users, who don’t possess technical data skills, to intuitively complete complex analyses without the help of IT, and to do it in real time. While the business intelligence and analytics category has traditionally been IT-led and required complex resources like data analysts and data scientists, the entirety of the data analytics market has now shifted to empower business users, without the dependencies of IT skills, to create and consume their own analyses. This “self-service” model has led to dramatic improvements in the time it takes to get answers from data, to discover insights in an engaging, interactive way, and in turn has dramatically reduced the bottlenecks associated with dependencies on people, skills, and process to get there.

    One such technology solution is Altvia Answers, a data platform for alternative investing analytics, purpose-built to integrate data from any varying source into a single platform. Combining Altvia Answers with AIM, the contact and data management for private equity and ShareSecure, Altvia’s LP portal, ShareSecure, enables a powerful, integrated system with immense capabilities for firms and investors.

    This may seem straightforward enough but consider each of these steps against the problems you’ve identified and the gaps in your current situation. Consider specific examples of data-related workflows such as fulfilling information

    To Sum It Up

    Now more than ever, industry-leading private equity firms are empowering their business users to discover insight in their data themselves, in record time, and in ways that seamlessly combine data across the entire organization. Thus, the data and reporting challenges they once faced are now forgotten, having instead turned their attention to how to take their newly-enabled capabilities on the offensive to beat their competition and to provide a new level of differentiated service to their partners.

    Technology Checklist

    What to ask when considering a technology solution for your firm:

    Is your technology solution an expert in your domain?

    What data would you like to have but don’t currently?

    Who are the stakeholders who stand to benefit from a data strategy? How do they benefit?

    Where could technology be used to facilitate internal processes that may be generating data that isn’t currently captured?

    Why is it of benefit?

    Where and how do you currently store data?

    Where are there problems in your ability to generate and capture data?

    How usable is your data in its current format?

    Which processes could be streamlined by or be better-informed by data?

    Who do you depend upon to make your data usable?

    Altvia translates data into intelligence. As a fully integrated Private Equity solution on the Salesforce® platform, Altvia combines technology with a proven process to harness the institutional knowledge of your communications, LP portal and back-end systems. Successfully raise and deploy capital, ensure compliance and deliver a trusted and transparent experience to stakeholders with a tailored solution from Altvia.

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