Ancient Greek philosopher Eraclitus noted that nothing is permanent except change. He was a man ahead of our time. We live in a world of constant change and a business climate empowered by rapidly evolving and emerging technologies. The only way to succeed and achieve excellence is by embracing that.
At times, though, change may challenge electronics OEMs as they struggle to effectively manage the vast amount of data fast enough, extracting value to meet the needs of their business. However, with the help of modern data architecture it is possible to access the most business-relevant information that will contribute to gaining greater competitive advantage in the market. First, though, it is important to apply the best practices for building an effective and efficient data supply chain.
The changing world of data
In the past, OEMs relied upon a single data warehouse. Later, the explosion of big data and its subsequent multiple data sources gave organizations access to more business-relevant information than what they could have ever imagined before. With this, the creation of a variety of data repositories for data storage and data analysis became a focus.
According to CITO Research's whitepaper titled Hadoop and the Modern Supply Chain (registration required), the data supply chain connects and feeds all different data sources into the modern data architecture. The modern data architecture includes multiple repositories, Hadoop, traditional enterprise data warehouses (EDWs) and other data stores, cloud data sources, public or open data sources, commercial data sources, data from mobile devices, sensors, and the Internet of Things (IoT).
Organizations are adopting modern data architectures in order to maximize investments by ensuring timely data is timely is available to start important initiatives such as business intelligence (BI) and analytics. With the ability to make more informed decisions, OEMs can capture competitive advantage and ensure healthy business development.
Mix & match solutions – avoiding challenges
Traditional warehouses alone are not enough anymore. They can't effectively move existing data fast enough to meet today's demands and needs. Multiple data sources need to be coordinated across departments and integrated to inform business decisions. To help with this and also to reduce costs, an increasing number of organizations are adopting Apache Hadoop. This open source, scalable software for distributed computing handles all sorts of data (i.e. structured, semi-structured, and unstructured) addressing both volume and complexity, and plays an important role in the data supply chain. Organizations can now keep more data on hand.
Yet, it comes with some challenges. Organizations need to be prepared to hire specialized expertise, or invest in training to build and manage the Hadoop infrastructure. Talent, though, may be scarce. According to CITO Research, for every one Hadoop expert there are 50 or more SQL experts. Finding the right people with the Hadoop and data skills required to fully leverage the platform can be challenging, so organizations need to start early enough for a successful deployment.
The best for organizations to get the most benefits for their data supply chain is to combine Hadoop with the implementation of data movement software solutions like Attunity Replicate, according to the paper. These solutions are specifically designed to make data movement to and from Hadoop easier, faster, and more cost effective, even across a broad number of platforms. They are designed to allow users to move data from one repository to another in a highly visible manner, unifying and integrating data not only from Hadoop but also from all the other platforms within the enterprise. This ensures enough flexibility in the data supply chain. When Attunity Replicate is combined with Attunity CloudBeam, it can also be used to move data to and from the cloud.
Speed data up & avoid risks
The paper also noted a number of risks that organizations may face if they are not able to move big data with the speed that is needed. These risks include:
- The inablity to execute business-critical big data projects
- A limited view of their business and all the data they have, which can lead to ill-informed decisions
- Laborious and manual movement of data
- Stale data; if not utilized quickly, data loses relevance and value
- Poor integration with legacy and other existing systems, limiting the scope of data
- The inability to create data lakes supporting high-level analytics
- Lack of effective data management and control, leading to misuse, or loss of data
- The inability to track data visually across the data supply chain to better understand use and validity
Assure flexibility & data agility
The adoption of modern data architecture allows organizations to create an efficient and flexible supply chain that supports easy data migration. This flexibility allows the organization to automate and move data quickly to supporting timely decisions that benefit the entire supply chain. Choosing the right solutions and hiring the right staff are critical to creating a fully operational and agile data supply chain that capitalizes on the breadth of available data.