As many as 83% of companies indicate that integration challenges are a significant barrier to their growth. In contrast, 97% of IT leaders note their organisations' problems with integrating data used by end users. What are the most serious data challenges influencing organisations and how to address them? Learn how to build the foundation you need to optimise Salesforce.
Key takeaways from this article
- Outdated or inconsistent information results in missed sales forecasts, ineffective marketing and the risk of errors in management reports, leading to visible financial losses.
- Managing data quality is an investment in competitive advantage and readiness for the implementation of advanced AI solutions, at the same time the lack of structured data prevents optimal use of analytical tools.
- Integration of data and systems is the foundation of an effective and profitable digital transformation – combining information from CRM, ERP and other sources, allows full insight into the customer and business processes.
In a world dominated by digitisation, data has become one of the most important resources. A lot of organisations refer to data as the ‘new oil’. On the other hand, they still do not treat the information collected in systems as a valuable asset. CRM systems, ERP systems, marketing platforms and various portals often store the same data that is not integrated. Just having a huge amount of data is not enough. Above all, it is their quality, availability and consistency that play a key role in building a competitive advantage. Lacking consistency and uniform standards, the value of the data clearly diminishes – it doesn't resemble ‘new oil’ any more, it becomes rather low-quality fuel from a low-profile, border station.
Why isn't data working for your success?
Although data is one of the most valuable resources of any company, many organisations are still not using it to its full potential. There are several major obstacles that slow down or completely stop business growth.
Lack of consistency and standardisation of data
Organisations are constantly collecting significant amounts of data in their systems – recent analysis estimates that nearly 90% of the world's data was generated in just the last two years. However, the lack of proper standards for storing them leads to a situation where the same information appears in different formats and structures in the organisation. Another disadvantage of the lack of standardisation is the difficulties associated with the analysis and integration of unstructured data. The variety of formats of names, addresses or phone numbers results in problems with identifying customers and drastically reduces the effectiveness of marketing efforts.
A lot of companies have databases that are not properly integrated, leading to difficult access to complete and up-to-date information, poor customer experiences, and preventing the company from accurately forecasting sales, personalising offers, and gaining insight into customer history. Today's customers expect a seamless, trusted service based on once-delivered data – any request to resubmit data or send an offer that is inconsistent with their history or their current situation is the easiest way to lose their trust.
As IT leaders point out, only 34% of organisations can say they operate on fully integrated systems responsible for customer service.
Problems with accuracy and completeness
The operational value of data drastically decreases when it is incomplete, inconsistent or inaccurate. Companies are often unaware of the negative impact that errors in their data have on sales processes or accurate decision-making. Business data loses its validity due to completely natural situations such as a customer's change of workplace, modernisation of the organisation's structure or a portfolio that changes over time. Without a proper model for updating data, systems begin to operate on outdated and useless information.
A common mistake in organisations is having the same data in multiple systems – and with different information about the same customer. Lack of a data management strategy means making wrong decisions and results in a negative user experience.
Only 10% of respondents say they do not face challenges related to data silos, while 74% of organisations perceive an overdependence of their IT systems. This kind of interdependence can generate various problems, including operational inefficiencies, data inconsistencies and difficulties in accessing and sharing information between different departments.
Poor data quality and business decisions
CRM systems of all kinds, analytical tools, marketing platforms, and especially AI models rely on available data. The result of insufficient concern for the quality of the information they have is erroneous conclusions. In any situation – regardless of the context – incomplete or incompletely validated data will result in a false outcome. With wrong assumptions, reliable results cannot be expected. In business, erroneous analysis manifests itself in the form of an incorrect sales forecast, underestimation of demand or improper segmentation of customers, which ultimately translates into ineffective marketing campaigns and unsatisfactory results of sales activities. In any organisation, the reporting area is essential – incorrect data leads to errors in financial and operational reports, and this definitely hinders accurate and profitable decision-making.
Regulatory requirements, such as GDPR, require an organisation to approach data in a reliable and up-to-date manner, and failure to comply can result in serious financial penalties and loss of customer trust.
Key steps to improve data quality
Identifying data problems is only the first step in accelerating a company's digital transformation. We will only benefit from the real value of data once an organisation has planned and taken the appropriate steps to organise, integrate and implement a data management strategy. The steps below provide the foundation for building a consistent and valuable data environment.
Step 1: Analyse the current state
The first step in improving data quality is to conduct a comprehensive analysis to identify major problems, such as inconsistencies, duplicates, outdated information or incompatibility with system formats. The analysis should include all sources of data in the organisation, its flow between systems and how it is used in business processes.
Step 2: Standardise and normalise data
We can speak of usable data at the level of the entire organisation when this data is consistent and complies with established standards. This includes, among other things, uniform formatting, definition of required fields for individual records, and elimination of ambiguities arising from different ways of recording the same information.
Step 3: Data integration
A vital step is to implement integration solutions, such as Mulesoft or Data Cloud, which allow automatic data exchange between CRM, ERP, marketing automation systems or e-commerce platforms. Eliminating silos allows you to gain full insight into your customers and business processes.
Step 4: Implement data quality management processes
Data governance should not just be a one-time activity, but an ongoing process. Implementing a Data Governance strategy includes defining responsibility for data quality, setting rules for access and updates, and regularly monitoring quality indicators.
Conclusion
Maintaining high data quality is a major prerequisite for carrying out a successful digital transformation. Companies that decide to take steps to integrate, standardise and manage data open the way to a real competitive advantage and create an environment that facilitates the implementation of advanced technologies. Taking advantage of the full potential hidden in data can be helped by a trusted and experienced technology partner who will not only analyse the current state of data in the organisation, but also design and implement effective solutions tailored to the company's business goals and budget.
If you want to learn how to turn data into real value and competitive advantage, we invite you to follow our upcoming articles on how to prepare your company to work effectively with Salesforce. Soon we will discuss, among other things, the impact of poor data quality on organisational performance and the real benefits of having valuable information.
