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Upping Your Data Quality With Four Key Data-Driven Strategies
September 4, 2020 Blog


Becoming data-driven has become an integral ingredient for business success. According to a McKinsey report, companies that use data for insight and analysis are 23 times more likely to acquire more users and 19 times more likely to achieve above-average profitability.

This success also depends largely on the data used in your data processing or analytical solutions. As the saying goes, “garbage in, garbage out”. If you want accurate data-driven insights for your business, then what you need is data of the highest quality.

But quality data doesn’t just “happen”. Just like refined oil, data needs to be cleaned, organised and governed before it can be put to good use. In fact, data scientists typically spend most of their efforts preparing data instead of working their analytical magic on the data. According to a report published by Gartner, data scientists typically spend most of their time (up to 80%) performing mechanical or repetitive tasks such as labelling and cleaning data, as well as building and selecting the right models.

Why should companies invest time and resources in enhancing data quality? According to Talend, a leader in data integration and data integrity[1], there are seven outcomes whereby data can add tremendous business value. These include:

  • Improved processes
  • Improved competitive position
  • New and improved products, stemming from better customer and market data
  • Informationalisation, or building data into products and services
  • Improved risk management
  • Reduced costs

Getting to each of these outcomes requires the use of different types of raw data within your repository. Once you have determined the data sources required, you can then work on integrating the data into systems that will be used by your data professionals. This doesn’t have to be a challenging or time-consuming process. You just need a robust data integration strategy guided by four key principles:

  • Consider the fundamental aspects of data integration and integrity product – First and foremost, you need a solution that can fulfil your data integration needs. With so many options in the market, choosing the right solution may not be so straightforward. Talend suggests that you take a look at six fundamentals: Pricing flexibility, transparency and certainty; Usability; API robustness; Open source expertise; Hybrid deployment options; Customer support and services.
  • Leverage the cloud to take advantage of all that data can offer you – Your data integration solution has to be able to run anywhere – on-prem and in the cloud – because data is omnipresent. Therefore, your data integration tool has to be able to combine the power of traditional data integration capabilities with modern and agile approaches by leveraging the cloud’s elastic scalability, cost-effectiveness and centralisation of semantics and collaborative capabilities which enable collaboration.
  • Ensure data integration strategy include proactive and pervasive data quality – This gets to the root of achieving high data quality. First, you need a solution that can proactively monitor and evaluate the level of quality of your data before it gets into your core systems. It must also be able to pervasively track data across your applications and systems and allow you to parse, standardise and match the data in real-time. These will in turn provide businesses with greater improvement and benefits from their data quality improvement efforts.
  • Ensure data integration platform includes data governance capabilities – Data governance provides many benefits. It provides a data map which enables an organisation to gain a deeper understanding of its data in order to improve both the quality and management of data. With good governance, the data becomes trustworthy, well-documented and is kept secure, compliant and confidential – all the requirements that will help the organisation meet the demands of different regulations.

By adhering to these four principles, companies can achieve both data integration and integrity, at scale and achieve success with their data integration initiatives.

For a more in-depth explanation of the four data-driven principles that will help you get to the complete, clean and credible data you need to make business-critical decisions, click here.