Big Data can deliver great advantages to companies from all sectors through the management of their market knowledge and their customers, consequently its management has become a priority within corporate digital strategies.
This is reflected in recent studies by IDC Consulting that, in addition to confirming its importance, estimate that services and technologies related to Big Data will generate turnover of 48,600 million dollars in 2019, with an average annual growth rate of 23.1% until then.
As a result, and to unlock the full potential of Big Data, companies need to opt for solutions able to maximise the power of the mass processing of large data volumes; without overlooking the need to define corporate strategies on the use of Big Data, the security risks or the importance of the quality of the data they must process. A good way to deal with these challenges, which affect the security, privacy and the nature of the information, is through an efficient Data Quality system, with sufficient capacity to optimise the potential of the large volumes of data which this technology integrates. In this way, having a quality database allows, for example:
- To promote cost savings.According to the technological consultant Gartner, poor data quality has a negative impact that costs companies more than 9 million euros per year. One of the main advantages of having high-quality data is the reduction in account expenditures, through a unified vision of credit control and invoicing, as well as the reduction in shipping costs. Additionally, savings can be made in productivity, since the staff involved in these tasks will not have to spend as much time in the revision of inaccurate or non-existent data, meaning their working day will be much more operational.
- Increase the effectiveness of marketing actions. When there are no incorrect addresses and returns, all mailings reach their recipient punctually, which is why, apart from reducing the expenditure on stationery and material, the effectiveness of all the campaigns also increases.
- Facilitate the loyalty and capture of customers.Nowadays, customers expect a successful and personalised shopping experience. The better the quality of the data, the easier it will be to deliver effective communications with this personal approach that the customers demand, which means opportunities to increase business value.
- Prevent fraud.A quality database permits a unique insight or 360-degree view of each customer. Legally, and faced with the obligation that organisations have, to comply with strict national and international regulations to mitigate and identify illegal activities, such as money laundering or fraud, having a precise picture of the customer’s data is a necessity.
- Achieving the targets of business intelligence.Knowing the status of a database (inconsistencies, errors, obsolete information…) results in a rapid monitoring of the changes in the status of the information (incorporations, removals, modifications…) so as to be able to make better strategic decisions. In this context, Data Quality has applications both in terms of staff efficiency and in the risk minimisation within an organisation.
- Help to improve the image of the company.What perception does a company offer when it sends incorrect information, misspelled names, incorrect abbreviations or outdated data? Data Quality is essential to obtain a single view, precise and reliable, of each customer. It is a question of transparency, consistency and reliability, that not only helps win the confidence of the user, it also improves the operational and transactional processes of any organisation. All these aspects positively contribute to improve the internal and external image of a company.
The key to Big Data, therefore, is in the quality of the information, not in the quantity. It is a question of obtaining the maximum return from the data processing, and to also stay ahead of the leading companies from the competition. To do so, Data Quality Systems become an indispensable tool to address management and decision-making in real time—or practically real—based on algorithms which, at the same time, rely on high-quality data.