As a business grows, the amount of data also increases. Maintaining important documents is necessary to keep business data safe and to maximize efficiency within a business. Document scanning service providers can convert your critical business documents into digital format. Proper scanning and management of data eliminates the need for paper, keeps files up to date, allows for quick reference and ensures streamlined workflow.
Earlier, data management was sloppy. Multiple copies of the same data are often found in the Production section as well as in other departments. Similarly, customer data is stored in different ways and no one is sure whether the data is successfully backed up or not. Today, with artificial intelligence, machine learning, and other advanced technologies, proper data scanning and management is possible. The application of AI depends on the accuracy of data used to train it. Machine learning and artificial intelligence need access to reliable data sources. The more data they access the more proficient they become at whatever task they are assigned to do. So, businesses are hugely investing in artificial intelligence and machine learning. Investing in new data management software and storage systems gives the advantage of cleaning data and also ensures that the data is constantly available.
In California, a workshop was conducted on Planning and Implementing a Successful Data Management Strategy. The panellists were Ed O’Brien, a principal at Competitive Direction and a Robotics Business Review contributor; Allen Thompson, director of knowledge management and analytics at Oneida nation Enterprise; and Kim Kaluba, senior product marketing manager, data management at SAS. They suggested some data management lessons:
- Understand your business goals before you plan: Before moving on to AI or data analytics or data strategy, find out what you do with the data. According to Kaluba, the key element is to align people, process and technologies. Although it sounds simple it is not that easy. Kaluba also added that big data strategy should be coupled with data management and analytics.
- Make sure that the data can be used, shared and moved efficiently.
- Data should be managed carefully as it is a corporate asset and it should be treated well by C-level executives.
- Establish common methods, priorities and processes to manage and share data in repeatable manner.
- Develop a strong internal data management team: Most of the companies do not document their data strategy goals. Time, money and patience are important to deal with internal fiefdoms. Thompson identified that many companies tend to buy software which seems to be a simpler option but they do not have a robotics officer or a chief data officer. Thompson added that it is not easy for a business to function without finance, marketing or other data. It is ideal to start at the departmental level because building consensus at a global organization sometime will not succeed.
- Begin with small wins and then move to bigger wins: Small quick wins help you get executive support while building toward longer term goals. According to Kaluba, IT asset management, robot management, condition monitoring and service-level agreements enforcement are examples of why Big Data is so crucial in manufacturing. Big Data is also important for sectors such as environmental science, midsize and multinational manufacturing, and telecommunications. Industry 4 initiatives need lots of data from supply chain management – and the applications keep increasing from robotic process automation to augmented reality and virtual reality. US retail supply chain operations that have adopted data and analytics solutions have seen an increase of operating margins approaching 20 percent. The use of data and analytics is not even, with retail industry capturing only 30 percent to 40 percent of potential value. The manufacturing industry has attained only 20 percent to 30 percent of potential value.
- Big data management and governance: Overload of data is an issue in every organization. Companies may have good data management strategies, but then data acquisition can lead to parallel projects and data. According to Kaluba, a major home improvement retailer wanted a single view of customer data to enhance customer engagement. They bought a master data management system also known as MDM, but even after five years and spending $200 million, it hasn’t been very effective. Master data management of data is important because it used for crucial decision-making functions. Therefore, it is important that duplicate data and such other errors are avoided. To be reliable, data should be accurate, complete and unified. Security is also an important part of data governance and many organizations fail to realize this.
- Data hoarding should be avoided: The process of collecting data continuously is an expensive affair. Day-to-day data mining is more important than gathering huge volumes of data and hoarding it to derive innovative insights.
O’ Brien observed that two thirds of the corporate data is stored in spreadsheets or databases that are siloed and not yet subject to data management practices. He further added that thirty three percent of C-Suite believes that their organization’s data is inaccurate and 77 percent of data users can’t access all relevant data they need; and 72 percent of organizations said that data quality impacts their business.
Thomson said that businesses collect transactional and structure data with the hope that it might be useful in the future but has no idea what problem data analysis could help. According to Kaluba, the components of data strategy are identifying the data that is collected, storing it, provisioning it as needed, integrating it to the enterprise and governing it.
All three speakers agreed on the fact that not every business problem needs big data and a company works best when everyone is on the same page. Companies are leveraging AI-powered analytics to optimize real-time business operations with exceptional detail, preciseness and impact. Successful data management requires active preoccupation of mainstream operations staff. Deploying intelligent document scanning and data management systems with the help of a data conversion service is also important for optimal performance.
The new digital age is likely to disrupt human processes, tasks and activities over the new few years as the technology continues to develop. However, soon many organizations are bound to realize that investing in AI alone is not enough; they will have to adopt good data management technologies to manage the huge amount of data that AI models rely on.
