Data Management refers to the practice of ensuring information is organized, maintained in order to attain an ongoing information lifecycle. Data management emphasis can be traced back to the beginning of electronics era where data was processed. Data management gets its roots from statistics, accounting, and logistics planning before computing begun (Briney, 2015). Data Management Association International has been looking for strategies to advance the approaches to data management. One of the approaches which have been put in place as a result is the master data management (MDM). Master data management is method used to enable enterprise links which are critical data to a specific file referred to as a master file. The master file provides a point of reference for the data. Other data management practices include; data stewardship, date governance, data quality management, data security management, and master data management (Briney, 2015).
Data is often viewed as a corporate asset. This brings about a major concern in regards to the responsibilities at hand. Professionals in data management have the responsibility of finding various ways to monetize the corporate data. There are various was that the corporate data can be monetized including; process streamlining, enhancing products which already exist, or selling the data (Salim, Johl, & Ibrahim, 2014). The need of effective data management has been on the rise in corporate institutions. This is basically because businesses are prone to increase in their compliance to regulations. The volumes of data being managed in organizations has rose creating a need for good data management strategies to be employed. This paper gives an overview of data management done at Ford Motor Company based on an interview with the Chief Technology Officer, Dr. Ken Washington.
The individual being interviewed was Dr. Ken Washington, the Vice President, Research and Advanced Engineering and Chief Technology Officer of Ford Motor Company. Dr. Ken Washington joined Ford Motor Company in June 2017 (“Dr. Ken Washington | Ford media Center”, 2018”). The role of Dr. Ken Washington is in leading Ford’s research organization, ensuring development and implementation of technological plans, and strategies while playing a key role of the company’s expansion. Dr. Ken Washington reports to the Ford CEO and President Mr. Jim Hackett. Ford Motor Company is an industry that deals with automation and it has been heading the artificial intelligence, complex ecosystems and platforms.
The interview with Dr. Ken Washington was conducted via skype. The interview lasted for forty five minutes which was later transcribed. Due to company policies, the policy documents were not shared. Despite the interviewee’s busy schedule, he was able to check the summary for accuracy and gave insights on how to edit it to perfection.
Ford Motor Company is working to attain the goal of automated vehicles. The vehicles will produce large amounts of data which will be processed and stored in the cars. One of the agendas of Ford is the innovation of autonomous vehicles which has the capability to change the relationship it has with vehicles. The technology being used to make autonomous vehicles a reality involves; processors, connectivity, sensors, algorithms, development tools, safety and mapping (Richard, 2002). Other than these specific technologies being employed, the unifying principle is data. The ecosystem will be getting data from sensors on the roads, data from the vehicles, the weather and communication from vehicles within range.
The interview with Dr. Ken Washington was done on phone. The telephone interview entailed collecting data through the responses from the interviewee to the respondent by using a questionnaire. Some of the questions were closed-ended while some were open ended. The telephone interview was pretty short and focused on the concentrated information.
Question 1: In what ways do you employ systems and tools for gathering data in usable and meaningful ways?
There are various benefits of using data collection tools in the workplace. The first important one is in identifying the most common injuries. Information can be gathered from Occupational Safety Health Act log by automating the process which would remove errors. This usually helps in creating a safe workplace needed especially for the Ford Motor Company. The other ways in which data gathering is important includes; it assists in developing better safety systems and cultures, ensures higher accuracy, and schedule maintenance.
Question 2: In what ways do you utilize systems and tools for warehousing data in usable and meaningful ways?
Warehousing data relies on the quality of the manager’s decision. Having the ability to have information sorted in a centralized system creates the building blocks for sound information and knowledge. Data warehouses contain information ranging from competitive intelligence to measuring performance. Warehousing data needs to be designed and implemented to be used in making sound decisions (Richard, 2002). The common themes of active data warehousing include; Business Value of Analytics, Single Version of Truth, Work Automation, Action Granularity, and Business value of Data Freshness
Question 3: In what ways do you utilize systems and tools for mining data in usable and meaningful ways?
Data mining is used to process information which is useful from the warehouse data. Data mining does not begin with having preconceived hypothesis. Data mining uses a wide range of systems and tools such as statistical analysis and symbolic methods (Ponce & Karahoca, 2009). The system and tools used to make data mining usable relies on the complex process model employed. The process includes; business understanding, data understanding, data presentation, modeling, evaluation and deployment.
Question 4: How do you use systems and tools for integrating data in usable and meaningful ways?
Complex data centers create the need for data integration. The data retrieved is usually used hence; it should be understood in aggregate. The system and tool used for integrating data have various key features and characteristic (Richard, 2002). Some of the features include processing data from a wide variety of sources like spreadsheets, enterprise applications, mainframes and proprietary data bases. Data is then semantically checked to ensure the data conforms to the business policies and rules.
Question 5: What are the systems and tools you use for reporting data in ways that satisfy reporting requirements?
Data can be extracted and presented in forms of tables, charts and other forms of visualizations by use of reporting tools. Reporting tools have business intelligence software that computes the data. The system and tool used for reporting data is the JReport which is embedded in advancing reports and technologies. The advantage of using JReport includes; value addition to the web portal, time saving, integrates and deploys rapidly, and it has a white label for seamless integration (Briney, 2015).
Ford has established and maintained an information culture in which data management are critical to strategic planning. Ford Motor Company gave executive support from the beginning. There are normalized data models which are being used alongside hands-on startup projects. Training is also provided to educate users on data management. Data management is critical to strategic planning as it ensures improved asset management, termination of unprofitable products, running the business, auditing bills, and being on top of litigation cases (Buffenior, & Bourdon, 2013). Ford Motor Company faces different situations such as readjusting parts of the trucks which is done based on importance of the manufacturing process at the time. Such decisions need to be made wisely and not by using the simple business rules such as first-in-first-out.
Summary and analysis of data management practice
Data management is important in strategic planning and decision making of any organization. Active data warehousing is still a new concept but it have significant benefits when designed and implemented (Buffenior, & Bourdon, 2013). Data mining is used to process information from the warehouse data. Reporting tools and the business intelligence tools are also an essential aspect of data management.
Data ecosystem is a complex process which involves both the government sector and the private sector. There is a need for private companies to partner up with the government as they develop algorithms and technology. The government is in charge of controlling the road and the policies which will allow autonomous vehicles.
Connect findings to literature review
Data management solutions have become expensive and incapable of coping with an everlasting data complexity. New demands keep rising given the new trend in business sophistication. Organizations believe in a single way of solving data problems as effective data management. There have been ambiguous regulations in the past without having proper structures in place. Research on data governance is minimal giving a gap for more work to be done in the field. Majority of existing work are descriptive literature reviews (Briney, 2015).
Active date warehousing is an important aspect for an organization to reap good benefits. Active data warehousing has not been implemented long enough but it has good benefits when implemented. Organizations are sometimes barriers to their own active data warehousing hence, there is a need to rethink how business is carried out. For a leader to make sound decisions and implementing strategic goals, they would need good data management techniques.
Briney, K. (2015). Data Management Resource for Researchers: Organize, Maintain and share you data for research success. Exeter, UK: Pelagic Publishing. ISBN-13: 978-1784270117.
Buffeenoir, E. & Bourdon, I. (2013). Managing Extended Organizations And Data Governance. In Digital Enterprise Design and Management 213. Springer, Berlin, Heidelberg, pp. 135-145.
Dr. Ken Washington | Ford media Center. (2018). Retrieved from https://media.ford.com/content/fordmedia/fna/us/en/people/ken-washington.html
Richard, H. 2002. Current Practices in Active Data Warehousing. Boulder Technology. Inc.
Ponce, J. & Karahoca, A. 2009. Data Mining and Knowledge Discovery in Real Life Applications. ISBN: 978-3-902613-50-0 dio:10.5772/62143
Salim, O., Johl, S., & Ibrahim, M. 2014. Holistic Approach to corporate governance: A conceptual framework. Global Business Management Resource. 6(3):2002-206.