This article was contributed by Star Media Group’s Senior General Manager of Group Analytics Freddy Loo and his Big Data team.

There are some prevailing myths surrounding "Big Data" that need to be addressed.

The term “Big Data” has been the floating around during much of this decade with the proliferation of mobile internet and the rise of the “FAANG” technology giants – Facebook, Amazon, Apple, Netflix, and Alphabet (formerly Google) – corporations that depend on understanding and rapidly reacting to the ebbs and flows of a global market.

While the notion of analysing extensive data sets in the name of more effective marketing strategies has proven difficult to ignore for large companies, the challenge for Malaysian property developers and real estate agencies in recent years has been one of balancing advertising and promotions budgets with achieving expected results in a climate of tighter profit margins – but the conventional formula of casting a wide net to attract potential homebuyers and property investors has been producing results with decreasing efficiency in recent years.

Juxtaposed against the allure of cost-efficient targeted marketing through data analytics is the idea that any organisation smaller than a global multinational corporation cannot afford to take advantage of the Big Data revolution – but that may not necessarily be true.

Myth #1: Big Data is for big companies

Perhaps it is in the name, but Big Data does not need to be petabyte-big for there to be some benefit to be had for small companies. The term Big Data implies that a whole lot of data – petabytes’ worth – is needed in order for a data set to be qualified as big, otherwise, it’s just plain old data (or so you might think).

The fact is, however, that the amount of data you collect and analyse does not need to be gigantic to apply Big Data analytics and data quality techniques to it. Whether you are working with a few gigabytes’ or petabytes' worth of information, you can derive value from any size of data set by developing systemic data management in addition to quality and analytical strategies for a set of data, then implementing them on an ongoing basis.

Most organisations have invested in some form of data gathering and processing capabilities for as long as there has been global industry. Data is not new tech – it has been around for a long time (remember Lotus 1-2-3).

There are always opportunities to leverage and reuse data sets of any size to gain some advantage. An organisation’s success in using data always lies in its ability to maximise existing assets and data to gain quick wins – even small wins will help to build confidence and spur the adoption of new strategies.

With actionable information derived from data, there is value to realise in decision-making, process improvement, and reporting – having the latest and greatest tech is not a requirement.

Given all the hype about Big Data and the fact that companies making the most noise about it tend to be tech giants like Google and Netflix, it can be easy to assume that Big Data analytics and quality solutions are only for large organizations that have a lot of cash to invest in Big Data strategies.

You don’t need to have a huge amount of cash or be a hot tech company to develop an effective Big Data strategy. Modern data analytics tools can be used in any organisation, at any scale, without a huge investment of money.

Indeed, what you can’t afford to do is not invest in Big Data, because doing so would leave you behind the competition. And fortunately, you don’t need to have millions of dollars on hand to start leveraging Big Data analytics, data quality, and data integration tools.

Myth #2: Big Data is an IT responsibility

Quite often, Big Data is seen as a matter for information technology (IT) departments – after all, you need hardware and software to implement a Big Data strategy. It is true that the hardware and software need to be developed by highly skilled technical big data employees (in-house or outsourced). This is nothing strange, as the required IT of Big Data is different from what we have had so far.

However, the required IT is merely a means to an end to achieve a Big Data strategy defined by the organization. This strategy could be “to increase customer satisfaction”, “to increase revenue”, “to improve the operational efficiency” – and the route to achieving that strategy could be Big Data or any other solution for that matter.

If the strategy is “to increase customer satisfaction”, it would be strange to define it as an IT matter or have an IT Director be the sponsor of the strategy. Therefore, Big Data is a strategic matter that should be dealt with, ideally, at the board level.

Data is the responsibility of multiple parties – consider, for instance, an Enterprise Resource Planning (ERP) system. While the maintenance of an ERP system is certainly the responsibility of IT departments – processes such as the computation of accounts payable lie with Finance departments and payroll with Human Resources (HR). Each department has very clear and distinct responsibilities – even when dealing with the same data set.

Data is sometimes owned by many parties – data sources may be managed by IT departments, but data processing and analysis lie within the purview of business users and data teams, while the actions to be taken are executed by front-liner (customer- serving) teams. Clearly, data is an asset that is owned by multiple parties – so who is the owner?

This is where it can get convoluted – which necessitates the creation of roles such as the Chief Data Officer (CDO) or Chief Analysis Officer (CAO). One thing is for sure – data is not owned solely by IT departments.

Myth #3: Big Data can predict the future

This is not completely a myth – but rather it is what some would call a half-truth. Correct use of Big Data can really give you some insights for predictions of the future, but these insights are based on historical data.

This means that the insights will depend on the data analysed and the requirements or the questions of the user. Therefore, Big Data is not 100 percent reliable for future predictions.

Big Data provides a lot of answers – but it won't answer anything if you don't first ask the right questions. Data on its own is meaningless and for data to be useful, you must first find the right questions to ask.

People think, “If we collect all the data, we can find all the answers.” But if you ask the wrong question and collect the wrong data, you'll never find the right answers. Just having data has no meaning unless you get value out of it.

So what kind of value are you getting from your data? Big Data may sound sexy, but it becomes very unappealing when you don't know what to do with it.

Data helps to prove or disprove a hypothesis of a business problem. Business owners still need to define the business problem and decide on actions based on the insights of analyses. Data is not a silver bullet but it definitely helps to expedite or provide clarity in the decision-making process as well as to measure a strategy’s effectiveness (for fine tuning).

Myth #4: Working with Big Data means hiring a Big Data scientist

There are many Big Data startups that offer innovative tools for companies as a SaaS (Software-as-a-Service) or DaaS (Data-as-a-Service) solution. For each aspect of Big Data (processing, storing, analysing, visualizing) there are different startups that offer such SaaS or DaaS solutions.

For these solutions, there is no need at all to build a Hadoop cluster – but there are three key talent profiles required for a successful data organisation: analysts (the suits), engineers (architects – or plumbers), and scientists (the mathematicians).

Data analysts define the business problem and apply the insights derived from data to the problem. Analysts should have the ability to execute strategies based on insights and track the changes iteratively. Analysts help to change business decision-making processes and behaviours.

Data engineers are techies tasked with sourcing, staging, harmonising, curating, managing and sharing data assets. These usually make up the largest teams and are the unsung heroes behind the scenes. Engineers do all the heavy lifting.

Data scientists translate numbers into insights. They work to analyse the data the plumbers create before sharing them with the suits. These mathematicians are much sought after for their skills but they definitely need to work closely with the two other teams to be effective.

Do we really have to take these myths into account when deciding on how to use Big Data?

The saying “You don’t know what you don’t know” is true in this world of Big Data. Without validating the myths against the facts, you would be buying a one-way ticket to a catastrophic failure. The best way to kick start a Big Data project is to set an overall goal that it has to achieve, gather all the available data, start small with free analytics tools that are in the market to prove the benefits – and finally, to steadily grow the project. The sooner you get started, the sooner you will be harvesting the profits.

Read on about using Big Data in the property industry, or signup to Propwall.my to access one of the nation's largest commercial sets of property data.