The New Profession in 21st Century School of Information Studies, Syracuse University The profile that I am really Interested in and aspire to have is Jonathan Goldman profile. HIS career track Is a typical example of a new profession In organization In twenty-one century, data scientist. Having a background in physics, with a doctorate from Stanford, Jonathan joined Linked in 2006 as an analytics scientist.

His responsibilities were developing core data sets from company’s user data, building mutational back-end engine for data mining and analysis with distributed data warehouse (Goldman, 2014). His works were used for Linden’s website and its search engine optimization to Improve user experience. His famous work were Linden’s feature “People You May Know’, which allows users to connect with people who may have same networks with them. He developed an algorithm to predict one’s network based on the information that the user entered in his Linked profile.

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The feature attracted millionaire views and helped Linden’s network grow significantly (T. H. Davenport & Patti, 2012). His following positions also focused on data analytics. As a director of Analytics and Applications at Aster data, he led a team to develop analytical applications using Aster Data’s high-performance analytic database systems to enable companies to gain Insight from their data. Being a founder of Level up Analytics, a big data consulting firm (was later acquired by Intuit in 201 3), he provided applied data analysis, strategic consulting services, and full development of data-driven products.

Now, as a director of data science and analytics at Intuit, he entities to build the next-generation, data-driven products for the company (Goldman, 2014). The strongest reason for choosing his career track Is that I have a deep Interest In the data analytics field. As a database administrator before, I learned how to manage and malignant database systems In the organization. Collaborating with my company’s technical teams to query data from database systems, I understood that data is only valuable if we can gain knowledge and information from them.

Historically, organizations have Business Intelligence (81) systems to 2 manage and get insights from their data. However, it was limited to structured data that was largely created or stored in large enterprise systems, such as Enterprise Resource Planning or Customer Relationship Management systems. Other unstructured data from social media, blobs, and geographical activities were sometimes dismissed even though they provide valuable information to the company.

Researching on practices on these complex data sets can help me identify needed resources to analyze and get insights from them. In fact, there are increasing companies trying to take advantage of these types of data. According to a study by IBM on big data, surveying businesses and IT professionals in 95 countries, most organizations are currently in the early stage of big data planning and development efforts (MOM, 2012). Hence, gaining knowledge and skills in the big data field would give many Job opportunities for me in the future.

There are several practices that will likely cause failure in this data analytics profession. First and foremost, data scientists will fail if their projects do not align with their company’s business goals. According to James Sulkies, an Vim’s expert in big data analytics solutions, if big data rejects do not have clear alignment with key business imperatives, such as differentiating in the competitive arena or enhancing customer loyalty, data scientists might fall in an “ivory tower”, a place that is disconnected from practical concerns of their company.

They would get lost amidst their data if they do not know what they are looking for. It is only a matter of time before their manager figures out that they are contributing nothing to the company (Jobless, 2013). Second, data scientists cannot implement their big data projects if their implemented systems do not innocent with existing company’s infrastructure. Due to organizational structure, historically, there were different IT systems used for different business functions. Data hence is locked in different lines of business as “silos”.

Parking big data in a new repository to remove these silos might come with a risk of introducing a new “big data silo” if 3 the new repository is not effectively connected to the rest of the business intelligence infrastructure of an organization (Woods, 2012). Not only data scientists cannot get enough resources to analyze, it also prevents their organization from taking advantage of their results. Third, data scientists will fail if they choose wrong data sources and samples to analyze.

For example, when modeling customer influence patterns, data analysts might arbitrarily limit themselves only to their company’s Internal customer data Wendell tanner are a lot AT external sources provoking customer behavior like social media activities. Neglecting these external elements connected to their organization may cause them to overlook key variables found in the under- represented segment and skew their data model (Jobless, 2013). Another failure reactive is that data scientists do not collaborate with their company’s business departments and managements.

A big data project often uses data from multiple sources across organization functions, such as IT, finance, and procurement. Disconnecting from these business departments, data professionals cannot collect business requirements in order to identify their organizational challenges. Last but not least, big data projects would not succeed if data scientists could not articulate their results to outside. It happens when they fail to visualize their founding in a Lear and compelling way that informs their company’s executives and product managers on the implications of the data for their products, processes, and decisions.

Based on these failure practices, and within the context of the four elements of information systems: system, process, technology and structure, I would propose some best practices that might lead to the success of data scientist profession. The first one would be gathering business requirements before gathering data (Pyromaniac, 2013). According to Piccolo, any information system should be built according to an explicit goal (or a set of goals) designed to fulfill the specific information processing needs of the implementing organization (Piccolo, 4 2007).

The system only succeeds if it is put in the context of organization’s culture, its strategies and objectives, helping its company achieve competitive advantages. Second, data scientists should take advantage of their company’s existing infrastructure. The company’s infrastructure, defined as the set of shared IT resources and services of the firm, constraint and enable opportunities for future information system implementation (Piccolo, 2007). They could be existing data warehouses or Bal systems, a team of experienced data analysts, or a platform developed to query data.

Data scientists should aware all of them in order to integrate their big data systems into their company’s infrastructure. Third, they need to build and develop teams of data analysts. Big data projects are often complex and require a variety of skill sets in statistics, computer science, and business. Hence, it is better to have a team of analysts that complement and help each other. Big data managers do not need to bring in entirely new people to their company, but forming teams of people with quantitative, computational or business expertise backgrounds (Davenport, 2014).

These people need to have strong business acumen, which allows them to get insights from company’s data. But most importantly, they need an intense curiosity, the desire to go beneath the surface of a problem, find the questions at its heart, and distill them into a very clear set of hypotheses that can be sees e (Davenport & Big data technologies anemia Tort project sun as Hoodoo and scripting languages could be taught or trained. Last but not least, in order to implement big data initiative, data scientists have to tie them to company’s C-level executives.

Many companies have recently recognized the importance of big data and spent their dollar budget for it. However, there is a mismatch between how money is being sent, and the support needed for activities that will create business value from data. Taking an example of Linked: when Goldman had an idea to find patterns that allow him to predict whose network a given profile in Linked would and it, he shared it to his 5 Linden’s engineering team. However, the team was uninterested and dismissed his idea.

They did not understand why Linked needed to figure out the network for its users. If Reid Hoffman, Linden’s expounder and CEO at the time, did not had faith in the power of analytics and gave Goldman a high degree of autonomy to implement his idea, there would not have the famous Linden’s feature “People you may know’, which helped Linked attract million users and gain competitive advantage over other social networks (T. H. Davenport & Patti, 2012).