Missing or incomplete datasets are a big problem that many businesses face – the lack of insight has the potential to cause disruption and challenges to the smallest of operations.
In our blog, we examine the importance of resolving missing data, the impact of missing data on a business, and how best to navigate the dangers of missing data with a long-term strategy.
Why does missing data matter in analytics?
As more and more businesses seek strategies based on data-driven intelligence, rather than instinct, the value of data has never been more recognised. Data can be used to great effect in almost all aspects of an organisation’s operations, such as:
- Providing insights on the current overall financial status.
- Informing strategies with predicted consumer behaviour and outcomes.
- Identifying new avenues for potential revenues, partnerships, and business development.
- Visualising key opportunities to increase internal productivity.
Understanding the dangers of missing data is more important than ever as an increasing number of businesses shift to a data-driven approach. But before understanding the true consequences of missing data, it’s important to understand what causes missing or incomplete datasets to begin with.
The causes of missing or incomplete data
Missing datasets can stem from a wide range of problems, but the underlying issue remains the same: unoptimised or abandoned data governance processes lacking consistent quality control constraints, alongside other fundamental procedures. Without correct data governance processes, or abandoned procedures, data silos can occur, while the overall quality and structure of incoming data is significantly lessened.
Internal teams may wish to abandon data governance procedures and BI processes for several reasons, including:
An overreliance on manual processes
Analysts and other team members that have grown accustomed and attached to the manual processes they are comfortable with using will often find switching to new, updated processes such as shifting to Cloud storage or utilising a Data Warehouse a difficulty, or even an inconvenience.
As a result, with some team members fully utilising updated processes while others continue to use manual processes, data may become siloed, disrupted, and even damaged – causing missing or incomplete datasets.
The mistrust of automated applications
Data may also become siloed if users are uncomfortable with placing their valuable data in the hands of fully automated applications. As BI capabilities continue to develop, this issue is only worsening.
Hesitant to trust the results gained through automation, users may instead develop their own separate spreadsheets, causing datasets to potentially be inconsistent, separate from the remainder stored in the single source of truth, and missing from further analysis.
The dangers of missing data
Missing data has the potential to disrupt the entire growth, revenue, productivity, and reputation of an enterprise. When constructing strategies based on incomplete datasets, analysts are not offered the wide perspective needed to make informed and optimised decisions.
Due to important data being missing, financial statuses or success of marketing campaigns may be incorrect, leading to the misallocation of resources and more. Additionally, with the awareness that some results are unreliable, teams may begin doubting the results of previously trusted intelligence – leading to organisation-wide distrust in any insights gained.
Navigating missing data
With the dangers of missing data providing a serious risk of jeopardising organisations, it is essential that teams resolve and assure that governance procedures are correctly, and consistently followed, and additional measures are installed for extra security. Three core tactics to ensuring that governance procedures are correctly adhered to are:
- Establish a Data Warehouse.
- Educate teams on the benefits of new processes.
- Generate trust in new processes through UAT.
1. Establish a Data Warehouse
Establishing a Data Warehouse is the first step in moving teams away from separate packets of data towards a single, unified centre of reliable and trusted information. With correct data governance procedures, teams can ensure that any data collected from the warehouse is reliable, while overall need to create separate packets of information will lessen.
Without a single point of truth, there is no definitive status or point of reference across an organisation. While creating a single point of truth can aid in reducing the chances of missing data occurring, it must be worth noting that this isn’t the definitive way of solving the issue, due to the prevailing issue of human error. If users fail to interact with a Data Warehouse correctly, issues can still occur – which is why the education of internal teams is a must.
2. Education, communication, demonstration
As we’ve seen, mistrust in automated functions and overreliance on manual processes are some of the core factors that lead to missing data, alongside inconsistencies in data governance procedures.
To ensure that teams are more willing to engage with new processes, such as automation and Machine Learning capabilities, education, communication, and demonstration must be prioritised.
Through educating teams of the many benefits and advantages that new systems may bring, alongside clearly communicating the importance of data governance procedures, and demonstrating both the benefits and risks first-hand, teams will be more willing to engage with optimised procedures. In turn, as well as encouraging a truly data-driven culture, this will reduce the risk of missing or incomplete datasets created.
3. Generate trust through UAT
Through encouraging a stage of User Acceptability Testing (UAT) when implementing a new tool, teams can interact with, get comfortable operating, and become confident in, new processes.
This will limit the chances of teams turning to legacy processes such as manual entry due to distrust or lack of confidence or capability.
The more that teams feel confident and comfortable engaging with new processes, the less the probability that they will revert to previous solutions, therefore the UAT phase should not be underestimated or abandoned.
Providing BI solutions with confidence
At DataShapa, we are committed to empowering truly data-driven insights and enabling trusted intelligence, with a capable team able to implement a wide suite of BI and Data Management tools. To learn more about how we can help your organisation reach the full value of their data, or for any queries you may have, you can get in contact with us here. We always aim to respond to any queries as soon as possible.
Alternatively, to learn how we’ve already helped a wide range of enterprises optimise their operations and strategy through empowered data read more of our case studies here.