Franchising vs. Licensing: Which Model to Choose?
Posted: Mon Dec 23, 2024 10:28 am
Big Data has been the investment choice of many organizations, according to the report “Big Data and Analytics Spending Guide” , carried out by the market intelligence company International Data Corporation (IDC). The forecast is that revenues in the Big Data and analytics market will reach US$ 187 billion by 2019, which represents a growth of 50% in relation to the US$ 122 billion in revenue in 2015.
But despite this being a trend, some companies still have difficulty putting their data to use. Many of them still have projects on hold, are planning to start them at some point, or are avoiding them altogether. And those that do take the risk and get Big Data projects off the ground run a high risk of failure, usually for the same reasons.
To prevent these failures from happening, we have listed the 6 main factors that can lead your Big Data strategy to failure. Follow the tips and minimize the risks faced by your company.
Content
1. Acquiring technology before planning what to do with it
2. Gather a mountain of data and then try to analyze it
3. Relying more on intuition than data management
4. Being ambitious beyond what you are prepared to face
5. Failing to train professionals
6. Not anticipating problems that go beyond Big Data technology
1. Acquiring technology before planning what to do with it
Before acquiring any technology, it is necessary to clearly understand the business context, what strategies are involved, what the priorities will be, and what problems the Big Data project intends to address. It is true that there will be many areas in which systematic data analysis can contribute, but it is necessary to establish priorities, after all, resources are not infinite. So, first choose the initiative that is most effective for the company. Once this first initiative is completed, move on to the second priority, and so on.
2. Gather a mountain of data and then try to analyze it
It is a mistake to gather a huge amount of data without defining a purpose for it. The first step should be to define the problem to be solved and then move on to the data. Start by canadian ceo email list asking what data will be needed to achieve the answer. Create a hypothetical scenario in which all the necessary data is available. Once you have identified the data, move on to the real scenario and ask new questions. What data is actually available? Is it local or off-site? Is it accurate? Can it be used without violating the law? This step also aims to find out whether the scope of the Big Data project is on track. In other words, these are iterative projects that are repeated if some of the primary data is not available.
Read also: HOW TO USE BIG DATA TO STRENGTHEN YOUR RELATIONSHIP WITH YOUR CUSTOMER?
3. Relying more on intuition than data management
Despite knowing the accuracy that data can deliver, many executive leaders tend to trust their intuition more than the results presented by data management. A survey by Fortune Knowledge Group found that 62% have this tendency and 61% say that, when making decisions, real-world knowledge is taken into account more than hard analytics. This attitude is a mistake, since data is collected precisely to contribute to more assertive decisions. If it is not used with this intention, then there is no reason to collect it.
But despite this being a trend, some companies still have difficulty putting their data to use. Many of them still have projects on hold, are planning to start them at some point, or are avoiding them altogether. And those that do take the risk and get Big Data projects off the ground run a high risk of failure, usually for the same reasons.
To prevent these failures from happening, we have listed the 6 main factors that can lead your Big Data strategy to failure. Follow the tips and minimize the risks faced by your company.
Content
1. Acquiring technology before planning what to do with it
2. Gather a mountain of data and then try to analyze it
3. Relying more on intuition than data management
4. Being ambitious beyond what you are prepared to face
5. Failing to train professionals
6. Not anticipating problems that go beyond Big Data technology
1. Acquiring technology before planning what to do with it
Before acquiring any technology, it is necessary to clearly understand the business context, what strategies are involved, what the priorities will be, and what problems the Big Data project intends to address. It is true that there will be many areas in which systematic data analysis can contribute, but it is necessary to establish priorities, after all, resources are not infinite. So, first choose the initiative that is most effective for the company. Once this first initiative is completed, move on to the second priority, and so on.
2. Gather a mountain of data and then try to analyze it
It is a mistake to gather a huge amount of data without defining a purpose for it. The first step should be to define the problem to be solved and then move on to the data. Start by canadian ceo email list asking what data will be needed to achieve the answer. Create a hypothetical scenario in which all the necessary data is available. Once you have identified the data, move on to the real scenario and ask new questions. What data is actually available? Is it local or off-site? Is it accurate? Can it be used without violating the law? This step also aims to find out whether the scope of the Big Data project is on track. In other words, these are iterative projects that are repeated if some of the primary data is not available.
Read also: HOW TO USE BIG DATA TO STRENGTHEN YOUR RELATIONSHIP WITH YOUR CUSTOMER?
3. Relying more on intuition than data management
Despite knowing the accuracy that data can deliver, many executive leaders tend to trust their intuition more than the results presented by data management. A survey by Fortune Knowledge Group found that 62% have this tendency and 61% say that, when making decisions, real-world knowledge is taken into account more than hard analytics. This attitude is a mistake, since data is collected precisely to contribute to more assertive decisions. If it is not used with this intention, then there is no reason to collect it.