Warehousing companies operate in an industry where loads of valuable data are generated on a daily basis.
And as those organizations continuously strive for improvement and becoming “smarter,” they constantly adopt new technologies, such as sensors, machines, devices, frameworks, and automation. In fact, according to Informa Tech, in 2016 the worldwide sales of warehouse automation technology (robotics, logistics, etc.) reached $1.9 billion in 2016 and are expected to reach a market value of $22.4 billion by the end of 2021
All those investments are the reason for warehouses to start generating even greater volumes of data, which are eventual prerequisites for data quality issues. And those are worthy of examining and looking closely into.
What are the most common data quality issues?
The different appliances in the warehousing plant generate data in distinct formats. Often, those are stored in siloed locations, where the data formats are not integrated because they are incompatible. Or, if they are mixed, the company bears the risk of generating low-quality analytics, which give a misleading view over the operations of the company, as well as on their clients, partners, finances, and industry events.
For example, the generated data through RFID, conveyor belts, orders handling robots, and IoT devices network are usually stored in separate storages, where the company has access to each of those individual and unrelated sources. But what happens if the management needs a joint view over the data?
The data scientists team has to work hard to equalize the format of the data sets, integrate them in one joint storage, prepare analytics, and derive insights from those. Not to mention that this process is extremely time-consuming. Any inaccuracy in this process results in misleading, faulty, inconsistent, and untrustworthy information.
Such inaccurate results have a negative effect on the planning and decision-making process for the development and growth of the business entity, which has a direct relation to its future success.
In some cases, the primary data sources may generate identical data, and create copies of the same data records. Generally, this is caused by the presence of data silos, which cannot communicate with each other in order to compare the stored information and identify any duplication of sets.
In warehousing entities that don’t integrate their data, this is a frequent issue, because comparing data manually from the silos is an extremely time and effort -consuming job. That is why the employees prefer to leave the silos as they are and move on.
Nonetheless, then the data is needed for analytics, this set duplication is a prerequisite for biased, exaggerated, or incomplete results, which bring no value to the company at all.
The issue with incomplete data arises mainly when the data is being moved for the primary source to the storage, or when it is extracted for analytics purposes.
In both of the cases, the quality of data is compromised because sets are missing variables, which causes the creation of outliers, gaps, and deviation from the original data that was generated. As a result, the moved data sets become untrustworthy and lose their business value because no insights can be derived from incomplete data resources.
Storing data for too long and not updating it regularly is a prerequisite for companies to use old, obsolete data.
For the warehousing businesses, having real-time data on hand is key because of the quickly changing external and internal factors. For example, updating the location of packages, or identifying the number of available inventory goods is crucial for smooth operations and preventing any delays or mistakes in order preparation, consolidation of products, etc.
In fact, “34% of businesses ship late because products are sold which are not actually in stock.” And if the warehouses have the right data, with the right quality available, such delays would be handled and avoided in time.
Having faulty data available means using, and potentially trusting, biased, obsolete, and flawed resources, which have no real value.
Data quality decreases when it is not shared efficiently across the organization. For instance, when the employees have access to only one specific part of the available data, they are enabled to use only that part for decision-making, planning, and risk assessment. And in that sense, they are allowed to look only partially at the truth, which data provides.
As a result, the data resources of the company become untransparent, untrustworthy, limited, and unsuitable to be used for their purpose: making the company smarter, better, and successful.
Solutions to tackle the quality issues
Break the silos
Breaking down data silos with high-quality data integration strategy contributes to solving all the data quality issues mentioned in the previous section:Improves data consistency
- Prevents the formation of incomplete data sets
- Reduces the risk of duplication
- Makes data easily accessible, as it is stored in one unified and uniform storage
And when having evenly distributed, organized, and formatted data, the warehousing companies benefit from higher-grade analytics, better control over their data resources, and improved transparency.
Making the data available to all of the company’s work-units improves the quality of data, as this way, all the employees are able to use data as a key tool for decision-making, no matter the organizational hierarchy level they occupy.
As a result, the data sets become more trustworthy and powerful, and the planning process in the different departments becomes empowered and driven by the available data. This contributes to better strategic decision-making, aligned with the corporate objectives.
Knowing the primary and end-point of the data transactions helps the warehousing business entities to have better control over their assets and prevent any data issues that occur throughout this process.
This results in having more consistent, meaningful, valuable and complete data on hand, and giving the Chief Data Officers knowledge to compare the original data with the formatted sets, recognize any malicious changes, errors and outliers, and tackle those to ensure the high quality of the data used for analytics.
Set guidelines for data management
When the data managers are aware of all the processes that data passes through, they have a better overview and control over its quality. Setting specific guidelines from data extraction to the analytics helps the warehousing businesses to identify if and where issues are arising, as well as to learn from those problems and prevent them in the future. What is more, they give a deep understanding of potential improvements and upgrades for better data management.
Such data governance system works to instantly see errors and avoid more complex problems in the future, in regards to faulty analytics, delays in data processing, low-grade availability, etc.
Comply with legal regulations
Even though many organizations want to sidestep the legal rules for data governance because they require investment, time, and effort, in fact, they can be a great way to ensure a certain level of data quality and security.
Generating, extracting, storing, and using your data sets “correctly” guarantees a clear overview of the data frameworks used in the business entity and facilitates data sharing and better framework communication with partners and other entities.
Having in mind the data quality issues which may arise in your company gives you a great advantage to strengthen the data framework used, apply smart solutions, and prevent the occurrence of any problems.
And by ensuring high data quality and transparency, the company can take great advantage of insightful, purposeful, and meaningful analytics, which contribute to better decision-making and planning, and facilitate successful and optimized future operations.