Understanding The Four Stages Of Data: Collect, Curate, Analyse and Act

Editor’s Note: To kick off our month of data-focused content publication, I’m delighted to introduce this vital intro to the topic from PRODA’s David Oates. In this piece, David breaks data down into its four major components, each one of which requires a deep understanding, and unique, nuanced processes and best practices. Only by successfully joining all four stages of your data strategy can you start to unleash its full potential.

Manual process are not just slow, the also raise serious concerns around accuracy.

The KPMG Global Proptech Survey, published in November 2019, stated that only 25% of respondents have a ‘well-established’ data strategy. This is a worrying statistic, particularly given that 52 percent of respondents also indicated that they have a structured approach to using data in order to improve their decision-making capabilities. The key reason for this gap is how difficult it is to access and process core data sets that are produced by legacy applications (for example, rent roll data), which are often generated in Excel from a Property Management System.

Data, on its own, is of limited use, yet the information it holds is powerful. To maximize the value held in the vast swathes of data that exist in Real Estate ecosystems, new processes are required.

Stage 1: Collect

The old real estate adage of “location, location, location” is also applicable to data. Core data is often stored in a multitude of places – personal and shared drives; data warehouses; as hard copies; and, increasingly (and most inaccessible of all), as email attachments.

The collection of data from all these various silos is difficult to make sense of and highly time-consuming to navigate. Couple this with a typically manual input process and the start of the data journey becomes incredibly tedious for all involved. Any data strategy therefore needs to have a standardised way of collecting and storing core data sets. This means that, as soon as data comes in, particularly from external sources, there needs to be a “drag and drop” ability to import data which then triggers the start of a workflow process that ensures the flow of data is consistent.

“To maximise the value held in the vast swathes of data that exist in real estate ecosystems, new processes are required.”

For example, if you are an asset management firm, then rent rolls coming in from operating partners or property managers need to be entered straight into a system for validation and analysis, before moving seamlessly to an underwriting or valuation model. Once a data strategy is introduced that has this collection point at its heart, the potential productivity gains are enormous and far outweigh the cost of implementation.

Stage 2: Curate

According to the University of Illinois’ Graduate School of Library and Information Science, “Data curation is the active and on-going management of data through its lifecycle and interest and usefulness to scholarship, science, and education; curation activities enable data discovery and retrieval, maintain quality, add value, and provide for re-use over time.”

In simple terms, curation centres around how we use and re-use data to generate real, meaningful information throughout a data set’s useful life. With the increasing sophistication of data management tools driven by machine learning, many of which now have a specific PropTech focus, this is simpler than ever before. Data can be stored, standardised into a common data model and enriched in such a way that it is current, consistent and usable. This means that the data sets become the gift that keeps on giving – there is less reliance on external sources for insight and therefore greater efficiency and reduced costs in the future.

Stage 3: Analyse

As far back as 2011, McKinsey produced a seminal report, “Big data: The next frontier for innovation, competition, and productivity”, which talked about the real value of data and analytics. The ability to identify trends – for example, track changes, make predictions, and add value to data – is now commonplace. It is this stage that really allows data to become usable and inciteful information. Ultimately it is the reason why there is a need to collect and curate data in the first place.

The ability to identify trends – for example, track changes, make predictions, and add value to data – is now commonplace.

In the Real Estate industry, analytics has traditionally been undertaken by people. Asset managers, investment teams, lenders, and insurers alike spend hours (if not days) analysing data sets in order to prepare monthly reports, investment proposals, loan recommendations, insurance underwriting documents, and more.

These manual processes are not just slow, they also raise serious concerns around accuracy. Trying to manually compare two cells in an Excel file with 20,000 cells is very difficult.  As such, there is an increasingly convincing business case to implement technologies that are capable of standardising and automating data analytics. Indeed, some  technology solutions are delivering productivity enhancements of as much as 90 percent.

Stage 4: Act

At its heart, the Real Estate industry is based on development ideas: smart investment and/or disposal decisions; intelligent construction choices; and ultimately delivering what the customer needs. Yet historically, these decisions have been based on little more than the experience of the practitioners charged with making the call.

“…no longer do Real Estate professionals need to rely on what feels right: instead their abilities and experience can be backed up – and even enhanced – by data.”

There is, of course, no substitute for experience, and highly skilled experts will continue to make sound decisions. However, their decision-making capabilities are boosted considerably when they can quickly extract actionable insights from the myriad of data points contained within rent rolls.

In conclusion, no longer do Real Estate professionals need to rely on what feels right; instead their abilities and experience can be backed up – and even enhanced – by the real information that can be unlocked when data is managed automatically, holistically and consistently.

Indeed, by automating the collection, curation and analysis of valuable data sets, all professionals can be empowered to make quicker, more accurate and – most importantly – more profitable decisions, and as a result, drive significant competitive edge.

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