10 Things you’re doing wrong to manage your organization’s data

10 Things you're doing wrong to manage your organization’s data
October 21, 2020 | Data Quality

10 Things you’re doing wrong to manage your organization’s data

More than ever before in history, organizations now have unprecedented quantities of information about their customers and their own operations. This information is used to gain insight into what the customer needs and where they need it and for how long. However, for organizations that have not yet embraced current data management systems, the precision of these insights has been limited by the amount and quality of data available to them. Below are avoidable approaches that are holding organizations back from reaping the benefits of their own data:

1. Continually merging outdated data with current data

To fully maximize the potential of big data, you must have high quality data available to the data analyst or the artificial intelligence system to be used. To achieve this, inaccurate and outdated data should always be removed from the data set being analyzed prior to reports being generated from the data.

2. Lack of clear definition of the data management concept

Before data is collected, organizations should have very questions which they’ll want the data collected to answer in the future. This leads to the better categorization and collecting of the most relevant data so as not to overwhelm the customer by asking for personal data that may never be used. Being lean and focused on data will save the organization time and storage costs while also ensuring the staff spend more time talking with the customer about what is important to them.

3. Lack of enough resources

Failure to assign enough resources to the data management endeavor makes it very difficult if not impossible to reap the full benefits of big data. Many companies fail to understand that just having a few people who ensure that no data is being lost or accessed illegally is not enough to achieve proper data management. You need specialists who can shape, simplify, and scrub irrelevant data so as to deliver a product that can be used in decision making. In most cases, the staff assigned are overwhelmed and don’t understand how to leverage the data they are protecting to the benefit of the company.

Know how to build a successful data quality team.

4. Poor data organization

This leads makes it difficult for users to visualize patterns and trends within the data. It is therefore important that all data is categorized well in a manner that clearly shows correlations so as to easily derive usable conclusions from the data. Artificial Intelligence plays a big role in this as some patterns may not automatically appear to the data analysts. It also provides an opportunity to raise alerts on live data that is being entered into the organizations database. By adopting a clearly defined structure of the data being entered, access is made easy and the potential for clear insight from the data is increased significantly.

5. Lack of a simplified structure of data

This is especially important if there are different entry points from staff and customers in different locations. The data structure can lead to system failures taking longer to correct or migration to a different system being delayed by confusion from the poorly structured data. By utilizing machine learning from the onset, the data management staff can be able to see weaknesses in the data structure and correct them before they become a major problem.

6. Viewing data management as an IT department problem

Many organizations will direct any issue that seems to be related to data to the IT department without going to the related departments. This lack of interest in how data is analyzed or handled leads to everyone in the organization investing minimum effort to understand data beyond what is on their screen at that particular time. In organizations that are data driven, employees will want to understand data availed to them as well as make recommendations on how this data can better be presented to them.

7. Lack of clearly defined relationship between data store in the organizations database

For example, some companies fail to relate new customers and customer retention to the marketing budget as well as strategy. In order to get a clear picture being painted by big data, there should be harmonization of data in the collection, storage, and access stage. By sharing more inter-department or branch data, staff from departments are able to exchange ideas and understand the overall strategy better.

8. Failure to leverage machine learning at the data entry stage

To ensure that only quality data is stored, it is very important that machine learning is introduced at all points of data entry in an organization. Incorrect emails, credit card information, names among others can lead to the organization having big data that is not very useful to their marketing efforts. By introducing artificial intelligence at this stage, it makes it possible to acquire quality data that can lead to companies delivering on better customer experience.

9. Failure to leverage artificial intelligence in data analytics

In today’s world of big data and rapidly changing customer’s preferences, it is important that the organization stays up to date on where their customer’s attention is. The use of artificial intelligence can help guide the organization’s marketing strategy regularly by constantly providing a possible analysis of where to get potential customers’.

Quick read: Why Artificial Intelligence and Machine Learning based data quality makes sense?

10. Lack of a clear data management policy by the organization

To harmonize all the data within the organization there needs to be a clear data policy on access, entry and storage of the data. ‘When?’ ‘where?’ and ‘who?’ is allowed to interact with ‘which?’ data should be clearly defined and all access events logged to ensure the data remains secure. A clear data policy would also help to ensure that the data management team process is always well funded to execute their mandate. This policy combined with machine learning can make it possible to detect possible hackers before they access the organization’s data.

Conclusion

Some organizations have had to continually grow their data management capability while others have opted for a complete overhaul of previously ineffective systems. Once the transition is completed, most will shift their strategy after gaining deep insight into their customers’ behavior trends that were not available to them before they invested in data management. To avoid missed opportunities on data that is already available, it is important to adopt proven data management strategies.

Schedule a demo of AI augmented data management platform to know how to manage your organization’s data and leverage immediate ROI.