Data Science is The Key to Bridging The Gap Between Tech and Business
Data scientists turn raw data into something that can be analyzed. The work they do is the key to bridging the gap between technology and business.
Data science has become an integral part of any enterprise because it solves problems. Companies can forecast the success rate of their strategies and, ultimately, manage operations more efficiently. At present, firms are data-rich. This means that they have a great deal of data that enables them to gain insight through proper analysis.
Data scientists are the ones who transform raw data into usable data. Corporations that employ the most data scientists are Amazon, IBM, and Microsoft. As far as location is concerned, America is the number one in terms of employing data talent.
Organizations that attempt to implement data management technologies into the business processes must ensure they have the necessary expertise and skill set at their disposal. Additionally, it’s important to try to bridge the gap between the two main domains involved in enterprise architecture: business and IT architecture.
If things work out between data scientists and business leaders, it’s possible to extract maximum value. Let’s understand exactly why data science matters and how can it power a business.
Solving the data silo problem
Data silos are non-negligible sources of inefficiency within an organization. They translate into missed warnings, lost opportunities, not to mention disparate strategies. A data silo can be defined as a repository of fixed data that an information system is incapable of analyzing. Silos tend to appear when departments unintentionally withhold data from other departments. You get lost in translation, so as to speak. Data generated from functional units such as finances, marketing, R&D, customer services, and so on, generate high amounts of data which could be used to improve business processes.
Only the experts can break down data silos and put them in the hands of the many – in other words, to enable analytics for everyone.
Just like other scientists, data scientists make and test hypotheses, learn from key findings and iterate. They use various tools and platforms to solve the issue of stifled collaboration and encourage knowledge-sharing across teams. Given that each data scientist comes from a diverse range of backgrounds and fields, many approaches are suggested for the same problem. Breaking down enterprise data silos can be realized as follows:
✔ Consolidating data from disparate sources and making data management systems stronger
✔ Making changes to corporate culture
✔ Incorporating the data silo problem into the overall strategy
It’s imperative that all causes be remediated. If there isn’t an infrastructure in place to allow the silos to break down, nothing will happen. The outcome is wasted time and incomplete projects.
Making a commitment to machine learning integration
It’s estimated that 80% of company data is unstructured. This includes the text contained in emails and various documents. In the old days, the only way to take advantage of this data was to read everything and process the information. Given that the volume of data is so high these days, it’s impossible for a person to undertake such a laborious task.
Machine learning, as well as natural language processing and text analytics, helps increase the speed of data assessment, not to mention that it reduces the regulatory burden.
“Natural Language Processing, NPL for short, helps computers obtain information from text such as emails and detect spam or make corrections. The input depends on the data output that is provided. The computer should be provided with examples of what to look for and how to interpret that particular aspect. Basically, you have to train a statistical model.”
– Terry Wimberly, Analytical Linguist at TrustMyPaper and Studicus.
To put it simply, it’s necessary to help the machine/machines find its/their voices. This has implications for everyone within the organization. While executives must have a clear vision and encourage a strong company culture, employees must do their best to adapt to the transition to machine-driven intelligent interaction.
Business optimization should be coupled with a machine learning model. If there isn’t a commitment to machine learning integration, then a project or practice is assigned to someone from outside the organization. This is unfortunate considering that machine learning is a standard requirement for data analysis. It’s necessary to have support from the executive level. Business leaders must understand that by making a commitment to machine learning, they become forward thinkers. Advantages include but aren’t limited to:
● Predicting customer behavior
● Putting an end to menial tasks (automation)
● Insightful business intelligence
● Developing a smarter workplace
Data scientists need machine learning to make high-value predictions that guide enterprise decisions and smart actions in real-time. Machine learning necessitates a base made up of two components, namely a strategic plan aligned with business objectives, an actual engineering platform, and the data to drive it.
When it comes down to implementing machine learning, or artificial intelligence for that matter, it’s recommended to replace the existing hardware with systems from leading tech providers such as Dell and IBM.
Directing actions based on trends
Analytics can be deployed for better decision-making. The data scientist goes through the organizational data and is thus able to recommend measures that will improve the firm’s performance, boost employee engagement, and increase profitability, which is the ultimate goal. Making decisions can be automated provided the right data is collected and utilized.
Executives are now capable of making better-informed choices and filter the information that they receive on a daily basis. Corporations need to understand that they compete in an algorithm economy and they must exploit AI knowledge. Various styles of data analytics are available, so companies are spoilt for choice. They can choose the model that best meets their needs.
Descriptive and diagnostic analytics, for instance, answer questions like “What happened?” and “Why did something happen?”. An ever-increasing number of companies arrive at a point when they’re required to use advanced data analytics. Over the past years, some of them have begun experimenting with DevOps, making sure everything is optimized and the DevOps are applied to big data projects.
Can companies overcome the data scientist shortage?
According to the experts at CIO, the increasing demand for data scientists makes it impossible for some companies to secure talent. What some organizations do is reach out to machine learning and empower non-professionals to build precise predictive models and free up data. This is a solution, but attention needs to be paid to the fact that this cutting-edge technology doesn’t replace humans.
It’s still necessary to employ data scientists. Working with institutions of higher education could turn out to be useful, as they have many data science programs. Anyway, the demand for data scientists isn’t going to reduce in the near future, so business leaders should really think about coming up with new ways to attract data experts.
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