Data Management Challenges for Supply Chain Management: Your Options.

Supply chain management is at the forefront of everyone's minds these days, as shortages of goods and services continue to have ripple effects across the globe.??

Across industries, Manufacturers, Distributors, Logistics and Transportation firms are still recovering from the aftermath of unprecedented pressures on global supply chains caused by the COVID-19 pandemic and subsequent lockdowns.?

To add to these challenges, businesses are now contending with international conflicts, high fuel prices, surging ocean container rates, and the ever-present threat of driver shortages.?

Amid all of this, successful firms are finding ways to leverage data and new technologies to contain costs, reduce working capital, streamline operations, and improve customer service.?In fact, a new era is emerging that will be powered by AI, machine learning, IoT, and other “smart” or "intelligent" technologies in what's being referred to as the "Machine Economy".?

It's not just hypothetical. These smart technologies are already permeating many aspects of physical supply chain management.?

Here are a few real-world examples:?

• Predictive Analytics: Advanced data analytics and machine learning algorithms are being used to forecast traffic congestion, identify maintenance needs, manage cash collections and predict customer demand.?
• IoT Devices: A wide variety of connected, internet of things (IoT) devices is being used on the shop-floor and in transportation and logistics, including GPS trackers, barcode scanners, and RFID tags. These devices are helping to improve asset tracking and performance management, as well as driver safety.?
• Autonomous Machines: Autonomous vehicles, drones, and robots are increasingly being used for tasks such as package delivery, warehouse inventory management, and even long-haul trucking.?
• Automated Customer Service: Automated customer support tools, such as chatbots and intelligent digital assistants, are enabling manufacturing and supply chain firms to offer instant, personalized real-time support to their customers.?

As the Machine Economy matures, businesses will continue to seek out new ways to use intelligent technologies, process and data automation tools to increase efficiency and improve customer service.?

Unfortunately, the volume and variety of data being generated by these new "smart" technologies is stretching traditional data management infrastructures beyond their limits. Without an adequate solution, these data management challenges threaten to stifle innovation and put the future of companies at risk.?

Top 5 Data Management Challenges for Supply Chain Management

1. Exploding data volumes?

Data is the lifeblood of companies across manufacturing, supply chain, transportation, and logistics. It is used to fulfil orders, track shipmens, plan routes, and manage inventory.?In the past, data was relatively static, with companies relying on manual processes to update and maintain their records. However, in recent years, data volumes have increased exponentially, as companies have started using sensors and other devices to generate real-time information about their operations.?This deluge of data has made it difficult for companies to keep up with the demand for information. As a result, many companies are struggling to find ways to effectively manage the data.??

2. Time-consuming data preparation tasks?

Data scientists, data analysts and business intelligence professionals still report spending around 45% of their time just on data preparation tasks. Data preparation is an extremely tedious and time-consuming and costly process that often involves:?

• Data discovery to identify data sources?
• Data profiling to determine data attributes and data quality?
• Data ingestion into data warehouses or data lakes using complex extract, transform & load (ETL) processes??
• Data cleansing to deal with data pollution and errors such as missing data, wrong data, or illogical data?
• Data enrichment to fill in missing information such as location or date?
• Data transformation to convert data from one format to another?
• Data preparation tasks are labour-intensive and error-prone, leading to issues with security and quality.
• Data accuracy can be compromised, data lineage is often unavailable, and data often does not reflect the latest state of the business.?

Increasing data volumes will only serve to exacerbate data preparation challenges, as data teams will be forced to wade through more data than ever before just to extract the most useful data insights.

3. Talent shortages

This assumes you already have a large data team of highly-specialised professionals who can do all this work. Demand for data science skills is outstripping supply, leaving many companies struggling to find enough talent.??

Data engineering skills shortages are also starting to emerge. Data engineers are responsible for designing data pipelines and data architectures that can handle complex data management processes.?

The lack of data engineers means that overworked data scientists end up spending more time on data engineering tasks than they would like, which slows down the rate at which they can extract valuable insights from their data.?The data science and engineering skills gap is widening due to several factors:

• Data skills are in high demand and data professionals can command increasingly high salaries?
• Data science and data engineering are relatively new disciplines, so the data talent pool is still small?
• Data scientists and data engineers require a diverse set of technical skills that aren't always easy to find in a single data professional?
• The data preparation process itself is labour-intensive and requires a lot of patience and attention to detail, skills that can take years to develop?

The talent shortage in supply chain industries is exacerbated even further by the fact that many of the most qualified data scientists and engineers are working in other industries, such as retail, finance and healthcare.??

4. Data access and security?

Data security is a major concern for all companies, as they are often handling sensitive information such as customer data, employee data, and financial data. Data breaches can lead to loss of revenue, loss of customer trust, and legal penalties.?

The challenge for all organisations is to find the right balance between data security and data accessibility. Data needs to be secured against unauthorised access, but it also needs to be accessible to the people who need it to do their jobs.?

These challenges are compounded by the fact that the data must be shared across a complex network of stakeholders, including government agencies, private companies, and international organisations. As a result, managing data in manufacturing and supply chain related industries is a complex and demanding task.?

Unfortunately, many organisations may not even know where all of their data is stored or if it's safe from unauthorised access. With so much data constantly changing—becoming more complex, diverse, and scattered across multiple devices—firms need a data management strategy that is designed to keep data safe, accurate, and readily accessible.?

5. Data governance and regulatory compliance?

Data governance is the process by which data is organised, managed, and controlled. This includes the identification, classification, and protection of data. The goal of data governance is to ensure that data is of high quality, and is used in a consistent and controlled manner, in compliance with all relevant laws and regulations.?

Manufacturing, supply chain, transportation and logistics industries are subject to strict safety regulations, which requires careful management of data related to accidents, incidents, equipment, and vehicle maintenance. Furthermore, the industry faces ever evolving and increasingly stringent environmental regulations, which requires detailed tracking of waste and emissions data.?

In addition to these specific regulatory requirements, all companies must also comply with general data privacy regulations, such as the EU's General Data Protection Regulation (GDPR).

Companies that fail to comply with data privacy regulations can face heavy fines, and their reputation may be damaged.??

With data volumes continuing to explode and the regulatory landscape becoming more complex, data teams will need to find data management tools that are not only scalable, but also flexible enough to adapt to data governance and compliance requirements.?

Your Options: Stack, Platform, or Builder?

Approach #1: The Stack

Traditionally, the data preparation process has relied on a highly-complex stack of tools, a growing list of data sources and systems, and months spent hand-coding each piece together to form fragile data “pipelines”.??

Approach #2: The Platform?

Then came data management “platforms” that promised to reduce complexity by combining everything into a single, unified, end-to-end solution. In reality, these platforms impose strict controls and lock you into a proprietary ecosystem that won’t allow you to truly own, store, or move your own data.??It’s clear that these old approaches to data management simply cannot meet the needs of data teams in the Machine Economy.?Data professionals are in desperate need of a faster, smarter, more flexible way to ingest and prepare their data for analysis, AI, and machine learning.?

Fortunately, there is a third approach.?

Approach #3: The Builder?

In order to overcome the data management challenges listed above, data professionals need a solution that meets all 3 of these criteria:??

Low-Code: It must be smart enough to build your entire data estate for you by automatically generating all the underlying code and documentation, from end to end.??

Agile: It must provide both technical and business users with a simple, drag-and-drop user interface for quickly ingesting, preparing, and delivering corporate data for analytics and AI/machine learning.??

Integrated: It must seamlessly overlay your data storage infrastructure, with no vendor lock-in, while integrating all the data ingestion, preparation, quality, security, and governance capabilities you need into a simple, unified, metadata-driven solution.??

Meet TimeXtender, the Low-Code Data Estate Builder?

Kompozable Ltd is proud to be working with TimeXtender which empowers you to build a modern data estate 10x faster by eliminating manual coding and complex tool stacks.??

With TimeXtender’s low-code data estate builder, you can quickly integrate your siloed data into a data lake, model your data warehouse, and define data marts for multiple BI tools & endpoints – all within a simple, drag-and-drop user interface.??

TimeXtender seamlessly overlays your data storage infrastructure and, with over 200 ready built connectors, connects to any data source, and integrates all the powerful data preparation capabilities you need into a single, unified solution.??

Because all code and documentation are generated automatically, you can reduce build costs by 70%, free data teams from manual, repetitive tasks, and empower BI and analytics experts to easily create their own data products – no more bottlenecks.??

We do this for one simple reason: because time matters.?

Learn How to Become Data Empowered with Kompozable and TimeXtender?

Come and speak to Kompozable at Smart Factory Expo, Stand E37 in Liverpool November 16-17, or?watch a demo to learn how we can help you build a modern data estate 10x faster, become data empowered, and win in the Machine Economy.?