Data has always been a vital ingredient for investment management. Reliable information, such as closing prices or positions, is a critical driver of business success. In a changing financial landscape, the need for data quality is assuming increasingly critical proportions and hence, a higher profile. While data has always been an indispensable requirement for investing, the need for good data is now more pressing than ever due to the increased focus on risk management; growing regulatory reporting demands and the search for alpha in difficult markets.

However, many companies still face real data management challenges, including data being trapped in silos; inconsistent reference data; few established standards for data; inadequate data governance models, limitations of risk systems in allowing proper analysis; as well as inaccurate forecasting of potential outcomes.

Here are five key steps to guide investment management firms towards the paths they should be taking:

1. Define data ownership. Firms need to identify their key data sets and make an individual or a team responsible for them. Data is like everything else - if no one owns it or is responsible for it, it gets neglected and loses its quality. Ideally, someone who understands the data and has the resources to fix any related issues should own it. Data ownership traditionally resided within the IT department, which did not provide an ideal scenario. Business units within the organization should ideally own the data, as each department would far better understand what the data means and what it should look like.

2. Improving the source and quality of your data. GIGO - 'Garbage in, garbage out'. This is a common mantra across all industries and it is a well-known point that if you put bad data in a process, you will only get bad data out of that process. The quicker you detect and rectify any defect or error, the cheaper it is. Within the investment management industry, the same holds true - getting a fund's net asset value (NAV) wrong or missing a corporate action can incur significant costs to rectify or result in missed opportunities. Data quality can normally be measured with three criteria - accuracy, timeliness and completeness. Data quality can be improved by ensuring the quality of data providers and by decreasing the reliance on single data providers. Having multiple sources of data will reduce any risks of downtime over the reliance of a single data source and will also provide a comparative benchmark to allow cross-checking of data against a second source.

3. Promote transparency. Transparency is being able to justify the level or value of any data attribute. One key area where transparency is crucial is pricing. For instruments where there is very little market liquidity, such as illiquid bonds, or where the markets are very opaque, such as complex or OTC derivatives, there is often no published market closing price or use to value a holding or position. In that case, the choice of what price has been used, be it a calculated fair value or an analyst-evaluated price, needs to be transparent to justify any regulatory, compliance or client queries.

4. Secure your data. Data is valuable and firms should ensure that only the right people have access to it. This is for a variety of reasons, including confidentiality, regulation, licenses and commercial value. In addition, market data vendors guard their intellectual property closely, and licenses are very proscribed, identifying specific purposes and user groups to use that data. Redistribution of market data outside the firm is a sensitive topic and rigorously policed. Breach of contractual terms can lead to significant retrospective charges, lawsuits and potentially having the market data service cut off.

5. Maintain flexibility. One thing is for certain and that is the constant change in financial markets. The events over the past six years show that the pace of change appears to have accelerated and that firms need to ensure that they can adapt to meet new circumstances such as changing market infrastructure. It is essential that data management too can change and evolve and any system you implement must be agile enough to handle the changes - be it new instrument types, changing workflows or procedures.

The financial services industry is amongst the most data-driven of industries. The regulatory environment these companies operate in requires these institutions to store and analyze many years of transaction data. On top of this, electronic trading has meant that firms both generate and act upon hundreds of millions of market related messages every day.

Many firms are facing real data management challenges and the need for good data is not new. Any business creating large data sets, including financial institutions, must embrace big data management.

Nick Quin is the managing director for SimCorp Asia-Pacific