WHAT IS DATA-DRIVEN BUSINESS:

Data-driven simply means applying or building a strategy based on the available data at hand.  

Data-driven business means business use a strategic process that leverages insights from data (including data collection, mining, management, and processing) to identify new business opportunities, provide better customer service, grow sales, improve company performance, and more. In data-driven businesses, the data is used by all members of the company, it allows employees from all different departments to use the database sources to analyze, thereby making judgments, predicting future trends and pursue business targets.  

ROLE AND THE IMPORTANCE OF DATA ANALYSIS IN THE INDUSTRIAL AGE 4.0

The concept of big data has been around for years; most organizations now understand that if they create data-culture in their business, they can apply analytics and get significant value from it. Even in the 1950s, decades before anyone said the term “big data”, businesses used fundamental analysis (basically numbers in a manually checked spreadsheet) to find insights and trends. 

However, the most important benefits of big data analytics are speed and efficiency. While a few years ago, a business would collect information, run analytics, and exploit information that could be used for future decisions; today, businesses are able to analyze bid data and identify insights for immediate decisions. The ability to work faster as well as saving time gives organizations an edge on the competition that they do not have before. 

Data-driven business. Source: nois.vn
Data-driven business. Source: nois.vn

Why is big data analytics important?

Big data analytics help organizations unearth their data and use it to identify new opportunities. That, in turn, leads to smarter business steps, more efficient operations, higher profits, and better customers service. In his report Big Data in Big Companies, IIA Research Director Tom Davenport interviewed more than 50 businesses to understand how they use big data. He found these following values: 

Cost reduction: Big data technologies such as Hadoop and cloud-based analytics technology offer significant cost advantages when storing large amounts of data – plus they can identify more efficient ways of doing business. 

Make faster, better decisions: With Hadoop’s speed and in-memory analytics capabilities, combined with analysis from new data sources, businesses can analyze information instantly – and make decisions based on what they’ve found. 

Create new products and services: with the ability to assess customer needs and satisfaction through analytics that provide the ability to provide customers with what they want. Davenport pointed out that with big data analytics, more and more companies are creating new products to meet customers’ needs. 

The Role of Big Data Analytics for Industry 4.0

The manufacturing industry is most impacted by trends and posibilities due to its nature and the large amout of data. Most manufacturers have only realized the potential of big data analytics, but there have been pioneers who have harnessed data analytics tools. Here are some typical cases of using data to run businesses from manufacturers. 

Risk Management

Data-driven business. Source: nois.vn
Data-driven business. Source: nois.vn

There are risks associated with industries when they change any of their business strategies or create a new product whether it’s a success or a failure. Before that, there were no platforms or tools to get a deeper understanding of it. Suppliers now can choose to share their product data with partners and customers, which creates complete transparency and a highly effective communication channel for both parties. This way, the manufacturer can see exactly whether the supplier has a production delay or just in time, in order to adjust all the processes involved and avoid waiting time. Quality data can also be shared in the same way; manufacturers can have all the quality metrics related to the production and products from their suppliers even before receiving the parts. By having better visibility into supplier quality levels and other performance metrics, manufacturers can have clear views of their supplier portfolios and insightful data at hand when negotiating supplier contracts. 

Build to Order Configuration

Manuafacturing ‘products to order’ has become a trend and not only in the automotive industry but also in aviation, computer services and even consumer goods. The build to order (BTO) approach is one of the most efficient and profitable business models. However, to view a real growth analysis from there, a well-defined data analysis platform is needed to analyze customer behavior and sales data. The manufacturer has all access to sales-related data and be able to perform accurate predictive analysis to forecast order volumes on each possible configuration and adjust their supply chains accordingly.

Data Analytics, Machine Learning & AI. Source: nois.vn

Improve product quality

Maintaining product quality is top priority for manufacturers. Most of them already have the data needed to improve quality levels and reduce quality-related costs, but only a few of them can connect their data sources in such a way that it will provide actionable insights. 

After-Sales Service

Warranty expenses and recall costs can easily get out of hand due to just the smallest mistakes in the manufacturing process. With the help of big data analytics, it is capable to avoid or foresee any warranty or recalls issues; saving signicantly money for your business.  

Tracking daily production activities

To optimize production quality and productivity, manufacturers need to have a daily flow of data from their production lines to see the differences and opportunities in real time. This includes sensor data coming from production equipment and financial information properly linked to operational data for analysis purposes. Employee data can also be tracked in real time by allowing for the exchange of employee card data with production line units. 

The growth of data-driven businesses

By using big data, it is easy to compare performance between different sites and pinpoint the reasons for the difference. In addition to internal production and sales data, it is also possible to analyse the entire markets, perform what-if scenerios and use predictive models. 

Preventive and Preventive Maintenance

When operational data is analyzed by sample identification, upcoming incidents and maintenance needs can be predicted. This allows preventing downtime and maintenance-related costs. At the same time, preventive maintenance will significantly prolong machines’ lifespan by preventing irreversible failure. 

Overhead tracking

Overhead costs are the costs incurred while creating a product, providing a service, or operating a division, that determine the profitability of each manufacturer. To have real control and visibility over these costs, bid data environments with connected data sources and advanced analytics capabilities is needed. This can hugely contribute to reducing supplier-related cost; saving costs and time for both businesses and their parners.  

Testing and simulation of production processes

The date of deployment of the product is when no risks can be taken. Both the manufacturing process and the product can be checked before being manufactured or started. This is possible thanks to digital twins, virtual reality environments, and manufacturing process simulation solutions. The use of such environments and tools can allow manufacturers to eliminate day-to-day risks from the decision-making stage. The purpose of this so-called digital transformation of manufacturing companies is to implement such data platforms that make the process of making decision and action strategy more rational.   

 

Logistics

The use of data in logistics is less common than in other areas of production. Warehousing and transportation are both areas where big data tools can be used to deliver big payback rates; howerver, there are still only a few companies around the world operating data-driven logistics services.

Data-driven simply means applying or building a strategy based on the available data at hand

Big data analytics help organizations unearth their data and use it to identify new opportunities. That, in turn, leads to smarter business steps, more efficient operations, higher profits, and better customers service. In his report Big Data in Big Companies, IIA Research Director Tom Davenport interviewed more than 50 businesses to understand how they use big data.