{"id":22754,"date":"2021-04-21T07:36:00","date_gmt":"2021-04-21T07:36:00","guid":{"rendered":"https:\/\/www.experfy.com\/blog\/6-reasons-why-organizations-fail-in-successful-big-data-initiatives\/"},"modified":"2023-08-24T13:58:47","modified_gmt":"2023-08-24T13:58:47","slug":"6-reasons-why-organizations-fail-in-successful-big-data-initiatives","status":"publish","type":"post","link":"https:\/\/www.experfy.com\/blog\/bigdata-cloud\/6-reasons-why-organizations-fail-in-successful-big-data-initiatives\/","title":{"rendered":"6 Reasons Why Organizations Fail In Successful Big Data Initiatives"},"content":{"rendered":"\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t\t
\n\t\t\t
\n\t\t\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Before the emergence of new technologies like machine learning, Artificial Intelligence (AI), and the Internet of Things (IoT)<\/a>, big data existed. However, these new techs seem to have taken over big data, and successful big data initiatives have become uncommon. Most of them failed to deliver the value they once promised. <\/p>\n

According to industry experts, 85%<\/a> of big data projects fail to meet their potential. But the problem isn\u2019t with the technology. Rather it lies in the companies\u2019 failure to harness the tech\u2019s full potential. This article highlights the reasons organizations<\/a> fail in successful big data initiatives. <\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t

6 Reasons for Successful Big Data Initiatives Failure<\/h2>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

There are several reasons why big data<\/a> initiatives fail. From mishandling technical realities to setting unrealistic goals, many things could go wrong. Below, we listed some of the top reasons for successful big data initiative\u2019s failure.<\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t

Lack of Infrastructure and Resources<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

When it comes to AI and Machine Learning (ML)<\/a>, companies like to set lofty goals. It usually helps them maintain their competitive advantage and move their business forward. But big data projects require a vast infrastructure and vital resources, especially talent. <\/p>\n

Lack of required talent is the greatest barrier to the implementation of big data projects. Since AI and ML job-specific roles<\/a> are in high demand, most businesses fall short of personnel. The problem is compounded by the fact that companies fail to make long-term commitments to big data.\u00a0<\/p>\n

Thus, they fail to invest in and train experts to help them develop big data initiatives. Therefore, it would help if companies identify employees who are proficient in statistics and computer science. People with these skill sets can receive training to perform data science functions and headline big data projects. <\/p>\n

Businesses have to recognize that new technological trends are not enough to ease out big data completely. Once they do this, they would be more committed to the success of their big data initiatives. Consequently, there will be increased business performance and more successful implementation of AI and IoT. <\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t

Unclear Plan and Vision<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

Business and dating on platforms like datingjet.com<\/a> have one similarity. You can\u2019t go into them blind; you need to have a plan and a vision. When it comes to big data, having a long-term strategy and vision is critical for project success. But what we find are businesses that have no long-term direction for big data. <\/p>\n

According to Allerin<\/a>, failure to plan and visualize comes from companies not understanding the capabilities of big data or the skepticism around it. Because of the preceding, businesses neglect their initiatives after the novelty wears off. Once the neglect starts, companies fail to allocate the needed resources, thereby ending big data initiatives prematurely. <\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t

Lofty Expectations<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

One mistake organizations make is expecting too much too soon from technologies. This leads to failure before they make progress. It would be best if business owners make their expectations without factoring in other companies\u2019 successes. <\/p>\n

Companies must understand the role of big data and the input of their staff in achieving success. Thus, when using big data applications, have proper analysis, testing, and training. Don\u2019t expect it to do wonders; limit expectations and take baby steps. <\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t

Applying It to the Wrong Problem<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

The primary function of big data is to help companies create new growth opportunities by analyzing problems and proffering solutions. However, most organizations fail to apply it to the right situation. Thus, rather than solving the issues, companies end up with more problems. <\/p>\n

To this end, it is crucial to ask the right questions and input accurate information. Keep in mind that for computers and tech in general, you would only get what you input into them. This follows the saying, \u201cGarbage in, garbage out.\u201d Lastly, the right question allows data scientists to create the appropriate algorithm to derive the right insight. <\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t

Inability To Move Models Into Production<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

This is one of the most common reasons why big data initiatives fail. After developing a model, most companies fail to move it into production. The problems come from the organization\u2019s IT teams not being equipped to handle the ML models. It again introduces the need to think long-term and adequately train employees to take on big data initiatives. <\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t

Management Resistance and Internal Politics<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

When there are insufficient organizational alignments, management resistance, and internal politics, big data initiatives fail. Employees often complain about their employers\u2019 failure to recognize the value of their services. These employers are the same people who earlier approved the big data project. Thus, business owners must do well not to hinder their big data initiatives if they want them to succeed. <\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t

Tips for a Successful Big Data Project<\/h3>\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t
\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t

There\u2019s no problem without a solution, and the following tips would help any company have successful big data initiatives. <\/p>\n\t\t\t\t\t\t\t\t<\/div>\n\t\t\t\t<\/div>\n\t\t\t\t

\n\t\t\t\t
\n\t\t\t\t\t\t\t\t\t