Digital transformation has been enhancing the business operations of all industry segments worldwide. One of the most happening ones is the supply chain & logistics domain. With the industry segment being quite volatile, stakeholders must maintain the speed and agility required to stay at par with the world.

Data, that is available in different forms, has been contributing bigtime to the day-to-day operations. It is getting tough for businesses and customers to extract the best out of data, with ease and convenience.  It is vital for organizations to delve deep into the data bulks and get the best results out of it.

And that can happen through a robust and scalable data lake. The world is moving towards implementing data lakes that can be obtained through the pay-as-you-go model. This help companies to makes best use of their data with the ideal data storage methodology. As compared to the traditional data warehouses, contemporary data lakes are the in thing now.

Earlier, data warehouses were the reliable approach for data management and organization. There was a lot of data related effort that was involved in it. Now comes the data lakes that have a centralized approach making it simple in taking decisions and processing applications. They act as a centralized repository for different systems that are implemented like ERP, CRM etc.

What is a Data Lake?

A data lake is a system or repository of data stored in its natural/raw format, usually object blobs or files. – Wikipedia

A data lake is a centralized origin that has all types of data stored in its raw form – be it structured, semi structured or unstructured data. It could also contain information from different systems that are prevailing in the organization. It empowers the data to be stored as is and does not force on any pre-defined structure. It is easily scalable and hence can mange huge volumes of data with ease and efficacy.

Some of the evident features of a scalable data lake are:

• Enhanced supply chain flexibility with in-depth data analytics
• Increased revenue and visibility into the inventory mechanism
• Accurate forecasting and predictive analytics
• Lesser costs with minimal complexity in data integration
• Fastened go-to-market time with pre-designed data components
• Agility, efficiency and scalability
• Easy access to data through innovative technologies
• Fasten deployment and avoid lock-in

Good Read: Data Warehouses Vs Data Lakes Vs Databases

Key Advantages of a Data Lake in Supply Chain & Logistics

Data warehouses and data lakes have both been fruitful in this industry segment. The best part about data lakes is that it facilitates the taskforce to select various storage capacities, technologies, metadata based on the needs of the system. Hence there is much more flexibility. Here are certain evident reasons why data lakes are now preferred in the supply chain & logistics industry:

• Minimal Data Duplication and Silos

With the usage of centralized data lakes, there can be more streamlining of access from different users and hence duplication of data would be minimum. There will be a single source of truth and hence data silos will not be created. There is enhanced security and privacy of information at all ends, offering more trust and confidence in the customers, than before. There is more consistency and effectiveness of data and processes than before.

• Faster Insights into Decision Making

Since the data lake streamlines data and the process, it becomes easier for stakeholders to search for their KPIs and their performance criteria to understand how they are performing. Users can gain detailed intuitive information from the data based on which they can take data-driven decisions that are best for the organization and suit the administrative goals.

• Advanced Analytics Through AI & ML Models

Since there is huge bulk of data coming through data lakes, there can be innovative AI & ML models that can be trained to garner advanced data analytics. This can help in coming up with best decisions for the business along with early identification of risks and challenges. The AI models are efficient and can help in garnering better performance. They also encourage moving into areas that have not been attended before, by getting insightful information from the models. Even exploratory analysis can be done through languages like Python for in-depth exploration.

• Lesser Dependency on Teams

Since the data lake offers unified access to all, the requirement of requests for access and depending on the teams becomes less. Users are independent enough to extract the data as they need it. Else, every now and then, they would have to go to the IT team to get the desired information. With data lakes, they are now self-reliant and confident of getting information themselves.

• Seamless Integration of Data from Different Sources

Data in data lakes come from disparate sources like CRM systems, ERP, GPS, sensors, IoT devices etc. Data lakes can consolidate and create a single source of truth that can lessen inconsistencies. It becomes easy for stakeholders to analyse and come up with different demand patterns, delivery route optimization, inventory management, costs and risks attached.

• Enhanced Data Quality, Security, Reliability

Since the data lake brings in consistent data from different sources, the level of security, quality and trustworthiness increases multi-fold. Users can rely on the data in the data lakes for their analysis. Without any pre-conceived schema, users can get the needed information that can help them take decisions with ease.

• Lower Cost, Lower Maintenance

One big advantage that data lakes offer to the supply chain and logistics industry is the minimal involvement of costs and maintenance. Since there is a lot of data easily available, the costs involved in searching for data everywhere is eliminated. It allows users to store and work with huge amounts of data with least expenditure and maintenance. Even data becomes logically correct and of use to the end users.

Till Later

Being an agile industry, the supply chain and logistics domain must possess a robust data management approach that aligns well with their organizational standards. Through a data lake, businesses can augment their operations in different ways as mentioned above. Creating a data lake is important since it includes selecting the apt data lake platform that can fulfil requirements.

Yes, there could be some challenges that can pose an issue – managing data governance and security, compliance with data privacy rules and regulations, integration of data from disparate sources, required skill levels in data science and analysis. But there is always a way out! The fact that data lake can work wonders in the supply chain and logistics industry does not change.

At Ridgeant, we have a widespread experience across multiple industry domains, supply chain and logistics being primarily one of them. Through our data and AI-driven solutions, we have perceived incredible outcomes in supply chain optimization for customers across the globe.

Our suite of data and AI-driven solutions empower your supply chain, confirming timely deliveries, cost optimization, and efficient operations. Right from optimizing routes to tracking real-time to predictive maintenance, our solutions possess it all. Even, as a trustworthy data partner, we enable global businesses to grab opportunities and steer impactful change.

Contact us and enjoy the experience of subject matter experts in the supply chain and logistics domain!