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Gartner, the world leader in unbiased technology research and advice, points out that as the digital world becomes more complex, leadership teams become increasingly dependent on analytics to help guide actions and decision making.
“Realistically, if we want to improve, we need to look at our analytics team. It is this front that unites the company's culture and strategy in the pursuit of achieving its goals”, says Gareth Herschel, research director at Gartner.
Analytics can be complex, but there are 10 megatrends that will help guide companies:
data for decisions
Companies need to move from “data-centric” to “decision- and change-focused”. It is necessary to understand why this analysis is being done and what its purpose is. The first step is to look at areas in the organization that need to be changed or that are already in the process of transforming. Every analysis will be used to improve a problem area and to influence and guide change, rather than remaining an analysis on which no one acts. It all starts with the transformation and the decisions associated with it.
Tactical to strategic decision makers
It is necessary to be aware of who is consuming the information within the organization. Analytics is valuable to everyone, from the CEO (Chief Executive Officer) to the lowest ranks, and should impact strategic decisions. To influence decision makers to make the best choices, you need to focus on three areas based on analytics: a balanced mix of strategy and financial objectives, realistic self-assessment, and objective review of business cases.
Fundamental to comprehensive functions
Analytics must include the company as a whole. “Each process, function, and individual becomes a consumer and user of analytics,” says Herschel.
Aggregate detailed levels of data
Analytical details allow for more personalized strategies. Subtle levels of granularity enable better understanding of customers and anticipation of challenges. This does not dictate a strategy, but rather educates decision makers on what the strategy might look like.
Data silos to multiply dimensions
Breaking down silos offers new perspectives and allows the team to combine multiple points of view to better understand what is happening, the likely reason why, and what action to take based on the analysis.
report to find out
Organizations must make a fundamental shift in thinking to improve engagement with data and be as curious about what happened in the past as they are about the future. This allows companies to understand the environment and potential, using the data to gain new insight.
Human to Artificial Intelligence
It is essential to have a clear and practical idea about the power of analytics and what it can generate for the business, regardless of the technique used. That's because they will do the same things, but with different levels of sophistication. Artificial intelligence (AI) is interesting and powerful, but it's not necessarily doing something radically different, it's just doing it radically better. You need to understand what's possible and then decide what level of sophistication the organization needs – be it a data scientist or an AI scientist.
Platform choice for analytics portfolio
The choice of platform will lead to differentiation. You need to select a platform that allows the venture to build something unique to the business that moves away from commoditized offerings. The company needs to decide whether to hire data scientists and a platform or whether business analysts with bundled applications are acceptable, as well as what roles external service providers will play.
From Standalone to Integrated Analytics
Analytics must be integrated into the accelerating business process. What previously took weeks in business now takes days, and what used to take a second is now measured in milliseconds. The analysis needs to be adjusted so that actions are taken at the appropriate time.
From confidential to open data
A streaming service decided to share data it had about ISPs (Internet Service Providers). It was the inside information that, when shared, generated a great deal of publicity and a good reputation for the company, as well as serving as public exposure for the ISPs. The service was interdependent across providers, but by publishing the data, the market dynamics were fundamentally changed. This forced ISPs to get better at something they didn't want to do when sharing information.
“Realistically, if we want to improve, we need to look at our analytics team. It is this front that unites the company's culture and strategy in the pursuit of achieving its goals”, says Gareth Herschel, research director at Gartner.
Analytics can be complex, but there are 10 megatrends that will help guide companies:
data for decisions
Companies need to move from “data-centric” to “decision- and change-focused”. It is necessary to understand why this analysis is being done and what its purpose is. The first step is to look at areas in the organization that need to be changed or that are already in the process of transforming. Every analysis will be used to improve a problem area and to influence and guide change, rather than remaining an analysis on which no one acts. It all starts with the transformation and the decisions associated with it.
Tactical to strategic decision makers
It is necessary to be aware of who is consuming the information within the organization. Analytics is valuable to everyone, from the CEO (Chief Executive Officer) to the lowest ranks, and should impact strategic decisions. To influence decision makers to make the best choices, you need to focus on three areas based on analytics: a balanced mix of strategy and financial objectives, realistic self-assessment, and objective review of business cases.
Fundamental to comprehensive functions
Analytics must include the company as a whole. “Each process, function, and individual becomes a consumer and user of analytics,” says Herschel.
Aggregate detailed levels of data
Analytical details allow for more personalized strategies. Subtle levels of granularity enable better understanding of customers and anticipation of challenges. This does not dictate a strategy, but rather educates decision makers on what the strategy might look like.
Data silos to multiply dimensions
Breaking down silos offers new perspectives and allows the team to combine multiple points of view to better understand what is happening, the likely reason why, and what action to take based on the analysis.
report to find out
Organizations must make a fundamental shift in thinking to improve engagement with data and be as curious about what happened in the past as they are about the future. This allows companies to understand the environment and potential, using the data to gain new insight.
Human to Artificial Intelligence
It is essential to have a clear and practical idea about the power of analytics and what it can generate for the business, regardless of the technique used. That's because they will do the same things, but with different levels of sophistication. Artificial intelligence (AI) is interesting and powerful, but it's not necessarily doing something radically different, it's just doing it radically better. You need to understand what's possible and then decide what level of sophistication the organization needs – be it a data scientist or an AI scientist.
Platform choice for analytics portfolio
The choice of platform will lead to differentiation. You need to select a platform that allows the venture to build something unique to the business that moves away from commoditized offerings. The company needs to decide whether to hire data scientists and a platform or whether business analysts with bundled applications are acceptable, as well as what roles external service providers will play.
From Standalone to Integrated Analytics
Analytics must be integrated into the accelerating business process. What previously took weeks in business now takes days, and what used to take a second is now measured in milliseconds. The analysis needs to be adjusted so that actions are taken at the appropriate time.
From confidential to open data
A streaming service decided to share data it had about ISPs (Internet Service Providers). It was the inside information that, when shared, generated a great deal of publicity and a good reputation for the company, as well as serving as public exposure for the ISPs. The service was interdependent across providers, but by publishing the data, the market dynamics were fundamentally changed. This forced ISPs to get better at something they didn't want to do when sharing information.
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