Data Science

ERP and Big Data

ERPs Make Big Data and Big Business a Good Match

ERPs Make Big Data and Big Business a Good Match 700 500 Xcelpros Team


What does “Big Data” mean to you and your company? To many, the phrase means large quantities of information from different sources, data that changes by the second. For example, it can refer to the temperature of a chemical process where a small variation makes the difference between good product and wasted materials.

A common big data definition is, “a collection of data that is huge in volume yet growing exponentially with time,” states. The “4 Vs of Big Data” are:

Figure: 14 Vs of Big Data

4 V's of BigData

  • Volume, in terms of data coming from multiple sources at the same time
  • Variety, which can be flow volumes, temperatures, production costs and other information calculated separately
  • Velocity, referring to the speed of information from application logs and device sensors (IoT)
  • Variability, data flows when a machine is running and stops when the production cycle ends

“Big Data” can also refer to lines from sales contracts referencing products, volumes and/or quantities from several customers. From a supply chain perspective, those same sales numbers require raw materials plus labor and machine operating time to produce them.

In the past, “Big Data” often referred to information from one department such as Production or Sales. One of the biggest challenges with big data is providing information siloed in one department to other areas that need it.

There are even challenges to searching big data, which includes getting results based on the query. When the query isn’t phrased correctly, or a required document has a naming error, important information is left out.

A key challenge is overwhelming volume.

  • The New York Stock Exchange generates one terabyte of data each day
  • Facebook cranks out more than 500 terabytes of customer-uploaded photos and videos every day
  • A jet engine generates more than 10 terabytes of data in 30 minutes of flight

By the Numbers

Many businesses are drowning in data, not all of which is useful.

  • 8%: the number of businesses using more than 25% of internet of things (IoT) data available to them
  • 10% – 25%: Marketing databases containing critical errors
  • 20% – 30%: Operational expenses directly tied to poor data
  • 40%: The growth rate of corporate data with a study by SiriusDecisions finding organizational data typically doubles every 12-18 months
  • 40%: the number of businesses missing business objectives because of poor data quality
  • $13.3 million: The average annual cost of poor data quality

Big Data Costs

Big Data comes with costs, especially for in-house networks. Once data is obtained, it gets stored before being analyzed. Data is usually backed-up in case something happens to damage, destroy or in the case of hacking, hijack it.

The actual costs of this data varies based on business size and need. Estimates place the lowest range at $100 – $200 per month to rent a small business server. Installation costs typically start at $3,000 and go up from there.

Big Data includes up-front as well as hidden costs. Up-front costs most people see consider includes:

  • Software tools to manage and analyze data
  • Servers and storage drives to hold the data
  • Staff time to ensure the physical devices work properly and to manage the data

These costs scale proportionally depending on the business’ storage and retrieval requirements and the processing power required to gather the data.

Hidden costs usually refer to the bandwidth needed to move data from one source or site to another. While we might consider it a simple task to download a movie on a cellphone, moving terabytes of data between servers can be significantly more expensive.

Accurately estimating big data costs is basically impossible without a detailed look at each company’s specific requirements and needs. However, online research estimates them to be anywhere from several hundred dollars per month for a small business to tens of thousands of dollars per month or more. Infrastructure costs alone can easily top $1,000 – $2,000 per terabyte (TB) with qualified outsourced consultant pricing averaging between $81 – $100 per hour.

Big Data Limitations

Having access to large volumes of data is great – when a company knows what to do with it. Especially when servers are in-house, big data has its limitations. These problems include:

  • Software tool compatibility, such as different types and brands of databases
  • Correlation errors, such as linking incompatible or unrelatable variables
  • Security and privacy from the standpoint of only exposing your data to the eyes of qualified people

From a mechanical perspective, one industrial device might use a Siemens programmable logic controller (PLC). Another device can use a Rockwell PLC and a third could be from Mitsubishi Electric. These different devices add additional layers of complexity.

Using supervisory control and data acquisition (SCADA) architecture is one way some larger companies are resolving PLC compatibility issues. SCADA includes computers, networked data communications and graphical user interfaces.

Figure: 2Big Data Limitations

Big Data Limitations

Resolving Big Data Issues

One way pharmaceutical companies can resolve rising big data issues, especially those caused by using older, legacy systems is with a modern ERP. Enterprise resource planning software such as Microsoft Dynamics 365 (D365) resolves many of these incompatibility issues.

Data integration is a major big data problem for companies that use one database in production and another in finance.

D365’s data integrator is a point-to-point integration service used to integrate data. It supports integrating data between Finance and Operations apps and Microsoft Dataverse. The software lets administrators securely create data flows from sources to destinations. Data can also be transformed before being imported.

Dual-write—a related D365 function—provides bi-direction data flow between documents, masters and reference data.

This type of data collection raises potential ethical issues when accessing large quantities of personal information, which could include contact information for patients enrolled in a new drug study.

Installations by professionals experienced in working with pharmaceutical companies can organize data and help strip out personal information. Removing it reduces the chance of a HIPAA (health insurance portability and accountability act) violation.

Being a cloud-based product, D365 also cuts down many of the personnel costs associated with big data management and maintenance. Microsoft assumes those costs along with the burden of data security.


Having a lot of information lets companies make accurate, informed decisions. Problems crop up when data is kept in departmental silos. Using an ERP to integrate information across departments removes many barriers to sharing information, which leads to more accurate sales and inventory predictions, reducing overall costs and boosting profits.

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key applications of data science in the biotechnology industry banner

Key Applications of Data Science in the Biotechnology Industry

Key Applications of Data Science in the Biotechnology Industry 700 500 Xcelpros Team

At a Glance

  • Genomics is an intricate facet of biotechnology that requires storing and analyzing humongous amounts of data. Cutting-edge data science trends have brought a transformation in the way genomics is studied worldwide.
  • Biotechnology deals with the extraction, reaction and manipulation of molecules in living beings. These processes will expedite safely and systematically with the help of the advancements in data science.
  • Every day, there is a humongous amount of data generated by various private and government-funded biotech firms. Data science is crucial in establishing interconnectivity amongst these biotechnology entities to share the knowledge database.
  • Companies are faced with newer variants of diseases every few years and this requires gene sequencing of microorganisms at a much faster rate to help in drug and vaccine development. Newer technologies developed in the field of data science can be pivotal to help with the endeavor.

Industries have advanced with the knowledge and application of biotechnology. From facilitating clinical trials of drugs and vaccines to genetic alterations in crops for better yield, biotechnology has been crucial in transforming the way we live our lives. Now, how does biotechnology cross paths with data science? The answer to this question is multi-layered. The last decade has seen unprecedented advancements in the way data has been leveraged, stored, analyzed, shared and applied to derive statistics, build forecast models and solve the mysteries of science. Data science is emerging in biotechnology with the tools and techniques that help scientists systematize their findings and expedite their experiments for better and safer results. Data science is the crucial bridge between knowledge and information. The application of data science is rapidly moving from simply analyzing the existing information to deriving solutions for the future.

By 2021, 66% of analytics processes will no longer simply discover what happened and why; they will also prescribe what should be done.Ventana Research Assertions

With data science becoming an integral part of how businesses operate, biotechnologists and related industries need to understand its emergence and crucial role. By understanding the applications of data-related tools and techniques, biotechnology can experience positive growth at a much faster rate. Let us look at some of the emerging pivotal data science applications in biotechnology.

1.Field of Advanced Medicinal Development: The field of modern medicine has gained momentum in terms of R&D with data science in biomedical research. Computerized medical records, big data to arrive at plausible diagnoses, automated medicinal kits, genetic coding, computerized molecular studies for drug and vaccine development, etc., are the technological boons of data science that have made it possible for companies to make unprecedented advancements in the field of medicine.

2.Developments in the Field of Agriculture:Scientists across the globe tout the systematic use of algorithms, computerized statistics and data analysis capabilities to advance the genetic studies of various crops. This has made it possible for them to experiment on the plants at a molecular level in the lab to shortlist the best yield. This saves time, effort and money while giving the best results with the combination of botanists, biotechnologists and data scientists. Apart from scientists, data science is also useful for farmers. Many mobile applications are being developed for the farmers to study best practices to grow crops, compare the prices and availability of seeds, fertilizers, and other farming essentials in their locality and get expert advice online. The data generated and leveraged in these apps can join together to create a library of references for the large farming community.

3.Mitigating the Damage to the Environment:Biotechnology is genuinely a way of giving back to nature by modifying existing systems and deriving new ones to minimize environmental damages. Now, one might ask what role data science can play in establishing ecological sustainability. Well, data is at the core of knowing the problem areas- factories with concerning amounts of emissions, agricultural sites that are in dire need of water conservation, landfills with immense amounts of non-degradable waste, etc. With the help of computerized tools, this data can be collected, stored and categorized to implement solutions. The solutions and best practices can also be shared via data science tools with other such areas, thus creating a chain of positive environmental practices.

Figure 1:Leveraging the Power of Data Science Applications in Biotechnology Industry

Leveraging the Power of Data Science Applications in Biotechnology Industry

The areas discussed above are prime examples of how data science applications are emerging in the biotechnology industry. Experts worldwide are working hard towards coming up with newer tools, techniques and solutions with which data can change the way biotechnology research is performed and results are derived. The coming decade will be vital in changing the way we apply data-driven analysis and solutions to bring about positive transformation in biotechnology and its related fields.

Key Takeaways

  • Data science has moved on from merely becoming an analytical field to becoming a supportive pillar for reasoning and research in the biotechnology industry.
  • Leveraging data with the latest tools and techniques is helping bio technicians in expediting research and development processes while mitigating manual errors.
  • Biotechnology applications in medicine, agriculture, environment and more such fields have transformed with the help of advancements in data science.
  • While data science is emerging in biotechnology, there are still many milestones to be achieved to unleash its unlimited potential.

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