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How Data Acquisition Enhance Business Development

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Though the collection and analysis of data has been with us as long as there have businesses interested in improving and understanding their processes, the Computer Age has seen a definite quickening in the sheer volume of data that can be gathered and processed. So is there really a point to being able to acquire data at a mind-boggling pace? In short, yes. A business’s essence is defined by the data it collects, but if you demand to know exactly how data acquisition can make a bottom line difference in an industrial business or application, keep reading.

Knowing the Unknown

Gathering data about something that is unknown is an excellent way to learn more about such disparate entities as scientific phenomena or new product designs. Often these patterns, especially in the former, are too subtle or take place over too long a time period to make intelligent suppositions. For example, it’s easy to reflect back on weather patterns over a decade just on the basis of personal experience, but the picture becomes less accurate when we try to assess long-term data that may go back a century or more.

The second example, new product design, allows engineers and designers to make tweaks based on data they collect without having to send out a terrible prototype into the marketplace for testing the hard way. This not only decreases time to market but avoids foisting something dangerous or poorly functioning on innocent beta testers.

Manufacturing / Quality Testing

Do the final products rolling off the end of the assembly line match up to the original design specifications for performance and safety? Unless your company collects this data, you’re left to make a biased assessment on the basis of what seems to be reality. Humans are notoriously poor judges in such matters. Data acquisition allows you to amass accurate records of how well the end result matches design expectations. It’s critical to know whether there is a gap that tells you the specs are unreasonable or the product needs improving. All of this points right to the heart of quality control. Doing this poorly can doom a business quickly.

Repair and Diagnostics

In the manufacturing and industrial world, it’s not always a simple matter to send a technician in to figure out why a piece of machinery is malfunctioning. Maybe the issue would require a complete dismantling of millions of dollars worth of equipment that would shut down the line for days or even weeks. This is the perfect scenario to illustrate how data acquisition techniques allow for the design of diagnostic systems that can figure out the problem in a fraction of the time it would take a human brain.

Monitoring

To expand on the previous section, wouldn’t it be an even better idea to use data acquisition to create and install monitoring systems that were designed to identify maintenance and repair issues or even system failures before they occur? Based on data collected during the times when machinery is running perfectly, the preventative monitoring of manufacturing or industrial systems could eliminate or at least greatly reduce outages and the accompanying downtime and optimize machine performance.

Automation

Often humans aren’t the best choice when it comes to operating complex or dangerous machinery over the course of an eight hour or longer work shift. That’s where data acquisition hardware and/or software can take the human element out of the loop, saving thousands of man hours annually for large companies. The Industrial Revolution has come and gone and it’s no longer a necessity for a human hand to be on the wheel of everything that happens during the manufacturing process.

Final Thoughts

To ignore the inherent possibilities of data acquisition is like trying to stop a speeding freight train by standing on the track with a stop sign. It’s time for entrepreneurs and CEOs to drop any lingering antagonism and take a hard look at how a mass of information can help their business massively.

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Understanding the Value of Data Acquistion

Those in the data science business realize that taking on important big data projects for business requires a structured process or life-cycle, to use a catchphrase, that includes five major stages. It’s the second, Data Acquisition and Understanding, we’re concerned with at this juncture. Within this stage there are three primary steps, or tasks to accomplish in order to meet the dual goals of 1) produce high-quality data that clearly relates to the target variables, and 2) develop a data pipeline solution that refreshes the data regularly and allows for artificial learning.

Ingest the Data

The first step in our Data Acquisition and Understanding process involves establishing a procedure that allows you to be able to move the data from where it is (the source location) to where you want it to be (the target location). Before analysis, training, or any sort of predictive activities can take place, be concerned with how you’ll be able to effectively select and move the data sets you need.

Explore the Data

Step number two in the process involves developing a solid understanding of the data. Data collected in the real world is far from perfect. It may be incomplete, full of distractions, or contain any number of other problems. This is the auditing process. It may be necessary to accomplish it in iterations before what you’re working with is clearly understood and you’re ready to introduce to the modeling process.

The reality is the data will likely need to be cleaned before it’s of much use. The phrase “Garbage In, Garbage Out” definitely applies here. The cleaning process is an article (or more) unto itself. Here are some of the tasks to focus on. Once you’re working with clean data, it’s time to step back and look for existing patterns. The goal here is to note any naturally existing connections between the data set and the target model you want to apply it to. Is there enough data to accomplish the goal and move forward?

The iterative nature of this step should be evident as well. You might have perfectly clean data but it just doesn’t match well with the modeling that is intended. There’s a distinct possibility you’ll have to go back and look for new or better data sources that will either augment or replace the first identified set.

Creating a Data Pipeline

After your data is ingested and cleaned, expect that the next step will be to create the process by which new incoming data is integrated into the working model through regular scoring and refreshing measures. In this sense, the data pipeline is simply an organized workflow that all team members are familiar with. It should be an automatic strategy that takes new data from various sources and prepares it for use in the ongoing learning process. There are various designs this pipeline might take. The three most common are:

  • batch-based
  • streaming or real-time
  • a hybrid of the two

Constraints of the present system as well as the specific needs of your business, obviously, play a large role determining the ultimate architecture of the pipeline.

data pipeline

Deliverables

As we come to the end of this stage, there are three deliverables that should be complete before proceeding to the next major stage, Modeling. They are:

Data Quality Report: This report should include attribute and target relationships, variable ranking, and data summaries at the least, but can cover much more ground if you need it.

Solution Architecture: This should contain a description of your data pipeline that you use to build predictive solutions based on new data after the model is complete. The pipeline to retrain your model on the basis of new data should be included as well.

The Big Decision: Some call this the checkpoint decision. The bottom line is that it serves as a place to stop and evaluate what you’ve done and what you expect to accomplish in the future. Ultimately, now is the time to cut short the project if the returns don’t justify the cost and labor time involved. Your basic choices are to proceed, collect more data, or give up the project.

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The Importance of Data for Your Business

Although it may seem that the concept of data collection is connected with the Information Age, effective business leaders have been collecting data long before the advent of technology. Business leaders have used that data to help them make decisions about their businesses. In the past, data collection was done manually.

A business might have questioned customers or clients in person or by taking surveys through the mail or over the phone. Businesses would use this data to create informed marketing strategies to reach more customers. However, because manual data collection was costly and time consuming, businesses had to rely on a small sample to make their decisions.

Now, with the technology available, it is easy to collect an ample amount of data to inform your marketing. In fact, you may find it challenging to sift through the data to understand what is relevant.

Bernard Marr, a data and analytics expert, said, “I firmly believe that Big Data and its implications will affect every single business – from Fortune 500 enterprises to mom and pop companies – and change how we do business, inside and out. Basically, no matter how small your business is, you do have a use for data.”

While data is important, you cannot rely solely on it to make your business successful. You must still work hard and make good decisions. Data is just one of the many resources you can use to grow your business.

Data and Decision Making

Even the smallest of businesses can create data. If your business has any sort of online presence like a website or social media account or if you accept online payments, then you have data that you can use.

big data collection

When making decisions about your company, you likely have a lot of factors that influence your decisions. You may rely on intuition or things you witness within your company to help you make decisions. Data can be a powerful resource because it gives you facts and figures to drive your decisions.

For example, when ordering inventory, you may make purchases based on what you have noticed has been selling well. Or, by using data you could have exact numbers on which items are selling and how many your have in stock. Data can allow you to make the most accurate decision.

You may worry that your business has yet to generate enough data, but Merit Solutions states that any business that has been going for at least a year has “a ton of data” to use when making decisions. People just need to know how to use it.

Any size business can use data for a variety of purposes, including:

  • gaining and keeping customers
  • improving customer service
  • marketing and social media
  • making predictions about sales

Data and Problem-Solving

When your business is having issues, such as slow sales or an unsuccessful marketing campaign, you will want to use data to help you figure out exactly what went wrong.

Analyzing data can help you learn exactly what your business is doing right and what needs to be improved.

Data and Performance

If you want to know how your company or even certain aspects of your business is performing, collecting and analyzing data can show you.

For example, when marketing, it is important that your campaigns earns more money than you spend on the campaign. Data can help you see if your marketing is worth the money.

Or, you may have a salesperson who you think is the best in your business, so you give him or her the best leads. Reviewing data may show you that another salesperson has a higher performance, but gets fewer leads. Knowing what is happening allows you to make informed decisions.

Data and Processes

Data can make a difference in your processes and let you minimize waste and lost time. Marina Martin says in her book Business Efficiency for Dummies,Inefficiencies cost many companies anywhere from 20-30% of their revenue each year.

Business Insider states that businesses waste the most money on bad advertising decisions. Data can help you to focus your advertising and maximize your ROI.

Data and Consumers and the Market

Data allows you to better know your customers and what they want. PayPal co-founder Max Levchin said, “The world is now awash in data and we can see consumers in a lot clearer ways.

Collecting data is essential to any business looking to gather and utilize real-world data to make smarter and more effective decisions. Collecting data is not a luxury but a requirement in the 20th century.