Seven challenges faced by data scientists and Solutions

Data science has revolutionized businesses AI, If you provide valuable insights , To make data-driven decisions , So data science has great potential for upgrading .

Every day , Organizations around the world are looking for 2.5 Trillions of bytes of data , To gain insight into their business and value driven actions . In order to achieve this goal , It requires highly skilled scientific experts or data scientists to participate in the development of enterprises in the business AI. In a growing field of business , Every action of the data scientist helps to improve the function of the business .

All professions will encounter certain obstacles or challenges , The role of data scientists is no exception . Many companies fail to take full advantage of data scientists , Put it in the wrong role or do not provide the necessary requirements . according to LinkedIn, The top ten skills of today's data scientists include machine learning 、 big data 、 Data Science 、R、Python、 data mining 、 Data analysis 、SQL、MatLab And statistical modeling . Most data scientists can use these skills in their computers ; However , These skills are not enough to put them in the right roles to achieve optimal business growth .

Let's discuss , Common challenges facing data scientists today .

1、 For intelligent enterprises AI Prepare the data

The most important function of a data scientist is to identify and prepare correct data . according to CrowdFlower A survey of , near 80% Data scientists clean up every day 、 organization 、 Mining and collecting data from different data sets . ad locum , Thoroughly examine the data , Then, it is analyzed and further work . This is a very difficult process ,76% Of data scientists think this is one of the worst parts of their work . Data contention requires data scientists to maintain logs to prevent duplication of data in the system , Different formats and code streamlining on different platforms TB Level data .

The best way to overcome this problem is to use technology based on artificial intelligence , Keep data scientists sharp and more powerful in their functions . Reinforcement learning is another multi-functional enterprise AI Tools , It can help and assist in data preparation , And provide insights into the problems of the opponent .

2、 Generate data from multiple sources

Organizations in various formats from different applications 、 Software and tools to acquire data . For data scientists , Processing large amounts of data is a huge challenge . This process requires manual input and compilation of data , It's very time consuming , And may lead to repeated or wrong decisions . When data is properly used in the enterprise AI The best function of , It is probably the most useful .

Enterprises can establish an intelligent virtual data warehouse with a centralized platform , Integrate all data sources into one place . Data from a central repository can be controlled or accentuated , To meet and improve the efficiency of the enterprise . This simple repair method can effectively save valuable time and energy required by data scientists .

3、 Identify business issues

Problem identification is an important aspect of stable operation . Before building data sets and analyzing data , Data scientists should focus on identifying key issues related to business operations . Before setting up the dataset , It is necessary to find the root of the problem , Instead of jumping to mechanical methods .

Data scientists can maintain a regulated workflow before initiating any analysis process . The workflow must take into account all business stakeholders and key parties . Special dashboard software provides a series of visual widgets , Can be used to make data more meaningful to the enterprise .

4、 Communicate results to non-technical stakeholders

The role of the data scientist is consistent with the business strategy , Their basic goal is to improve decision-making in the organization . The biggest challenge for data scientists is to communicate their results or analysis with corporate executives . Most managers or stakeholders do not understand the tools and equipment used by data scientists , therefore , In order to pass through the enterprise AI Implementation model , It is important to provide them with the right basic ideas .

Data scientists need to adopt some concepts , For example, data telling stories , Provide a powerful narrative for their analysis and visualization of concepts .

5、 Data security

Rapid upgrades move organizations to cloud management to store their vital data . Cloud storage is threatened by network attacks and online spoofing , Make confidential data vulnerable to external attacks . To prevent these cyber attacks , Strict regulations have been implemented to protect data in the central repository . The new guidelines force data scientists to bypass these new rules , Make their work more complicated .

In order to overcome the threat to security , Organizations must install advanced encryption and machine learning security systems to protect data . These systems must comply with all safety regulations , To avoid time-consuming audits , To improve operational efficiency .

6、 Efficient collaboration

Data scientists often work with data engineers on the same projects for organizations . So good communication channels are essential , To eliminate any conflicts . The organization shall take measures to establish good communication channels , To ensure that the workflows of both teams match . An enterprise can also set up a CEO to supervise whether the two departments work on the same line .

7、 Nonspecific KPI Selection of indicators

There is a misunderstanding , Think that data scientists can do most of the work alone , And provide ready-made solutions to all problems faced by the organization . This puts enormous pressure on data scientists , It also reduces its work efficiency .

For every organization , It is essential to have a set of defined indicators to measure the data and the analysis proposed by scientists . Besides , They must examine the impact of these indicators on business operations .

The work of data scientists is a challenging one , Because there are various tasks and requirements . However , It is one of the most demanding jobs in the market today . The problems faced by data scientists can be easily reduced , To improve the enterprise AI Productivity and functionality in demanding work environments .