Data Science Innovations: 5 Main Obstacles in the Implementation Process and How To Overcome Them

Data science is a rapidly developing sector of study. Its main goal is to translate vast amounts of records into valuable business insights. Implementing data science-based tools into your company can be highly beneficial. AI software is more efficient and accurate than humans have ever been.

It all sounds great, so how come that only a minority of companies on the market are using or working on the application of data science solutions? Well, the process is not as simple as it may seem.

Time-consuming cleaning and preparation of data for analysis

The first and crucial step in order to use the full potential of massive amounts of data is its preparation. The process involves finding inadequate records and correcting or removing them from the base. Data cleansing is a mandatory process – otherwise, you can end up with false conclusions or predictions.

To cut on the painstaking job you can use special tools. The most popular ones are Trifacta, OpenRefine, Paxata, Alteryx, Data Ladder, WinPure.

You can also take advantage of libraries, for example, Python, Dplyr, or Pandas.

The need for context of data to fully use the potential of AI capabilities

Context analytics are based on understanding the importance of contextual history secluded from insights about entities in the process of finding conclusions beneficial for the business.

By connecting big data with context, it is possible to determine patterns, trends, and relationships from both structured and unstructured data of an enterprise.

Digital Transformation – yes, but for whose benefit?

Naturally, first in the line for profits is the very company that initiated a digital revolution are leaders, but what about other sides involved like employees, distributors, customers, etc. If none of them gain any benefit they’ll perceive the transformation only as an additional problematic change.

While implementing AI solutions into your employees’ habits make sure to first gain their trust in the new technology. Don’t expect them to get all excited about the extra work you’re putting on them without giving a reason that really speaks to them. Remember, you force your team to use tools that may have enough potential to replace them in the future.

Without the engagement of both the company and the rest of the sides involved in the process, digital transformation can become a failure.

Need for qualified data analysts

Data analyst plays a significant role in every company focused on development. This job position requires certain skills that an employee should master in order to make a real impact on the company.

The key features your future data analyst should possess are:

  • understanding how your company works, knowing its business strategy, market position, targets, both strong sides and weaknesses
  • technical skills, understanding of data value chain
  • people skills, communicate both with colleagues and data suppliers, understanding which information is safe to share and which are not
  • critical thinking, investigating the significance of particular information
  • ability to visualize data and its meaning to share insights

Unaware CEO’s

If you want to have a successful business that uses the full potential of data science analysis you have to start at the top.

Only a company run by a CEO who understands the significance of data management and has enough knowledge to be an active side in a conversation concerning modern analytics like the usage of AI, for instance, can make smart decisions positioning their business on the top of its field.

Data Science transformation – 3 tips on how to take full advantage of it.

  • Ensure an easy access to data for as many decision-makers as possible. Their influence determines a success or a failure of your business.
  • Stay flexible. The speed of data, inputs, outputs, and requirements will change every month, and to take full advantage of new information you have to be prepared to make changes frequently in your data strategy.
  • Start with a small project that essentially resembles the crucial purposes and values of your company.

The process of successful implementation of data science into your business may turn out to be problematic. Concentrate on a project where you can measure the possible outcome and which you can complete within a year.

Data science can be applied in diverse industries, from retail to finance, and many more. The possibilities are limitless. The biggest players on the market are already aware of the benefits data science has to offer. SMBs are observing and learning from their mistakes.

Soon, complex data analysis with AI solutions will become a standard – necessary to stay and grow in the market.

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