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Common mistakes in business analytics

metrica de las empresas para avanzar en el mercado

There are many ways to work with data, which is why company leaders are advised to explore as many possibilities as possible. Each presents different opportunities for profit and competitive advantage, from product improvements to new sources of income and industry change.

A team of data scientists can do a series of clever analyzes to get an important insight, but that idea will die before it is born if others in the organization do not pursue it, by developing a deeper understanding of the implications, thus taking a critical decision and taking advantage of customer interactions.

Does your company know what to do with all its data?

McMillan constantly invites the advisor to “imagine you have a conversation at 6:00 PM every afternoon with a Harvard MBA with 800 years of experience.” Think about your clients’ opportunities at night, and in the morning, present a list of the 10 best actions for the day. The goal is to develop personalized strategies for each client based on a lot of data. Wouldn’t this help make your customers happier?

Great ideas can emerge from this process, improving products, services and processes. For example, at Morgan Stanley, Jeff McMillan improves working relationships with his wealth management clients by analyzing everything: client objectives and portfolios, available investment products, and emails. An algorithm then takes this information and suggests actions, so advisors can choose what is best for their clients.

McMillan invita constantemente al asesor a “imaginar que tiene una conversación a las 6:00 PM todas las tardes con un MBA de Harvard con 800 años de experiencia”. 

Everything a company does, from product delivery to space management, uses huge amounts of data, so quality must be proactively addressed, eliminating the causes of errors. Bad data makes this job more difficult and increases the cost by up to 20% of revenue.

Todo lo que hace una compañía, desde la entrega de productos hasta la administración del espacio, utiliza enormes cantidades de datos, por eso se debe abordar de forma proactiva la calidad, eliminando las causas de errores. Los datos malos hacen que este trabajo sea más difícil y aumenta el costo hasta en un 20% de los ingresos. 

Most companies don’t often think about selling their data, but doing so can provide a great opportunity. For example, auto insurance companies discovered relatively simple information they could sell: the number of new policies written every day reflects the health of automakers.

Because each sale requires a new insurance policy, the number of new policies issued each day provides a faster indicator. This becomes a stream of profit for issuers, who aggregate this data across the industry and package it.

Using data to its full potential is more related to management than to technology.

What is your data strategy?

Cross-sector studies show that, on average, less than half of an organization’s data is actively used to make decisions, and less than 1% of unstructured data is analyzed at all. More than 70% of employees have access to data they shouldn’t, and 80% of analysis time is used to discover and prepare data.

The defense and offense of data are differentiated by business objectives and activities designed to address them. The defense tries to minimize risk, ensuring compliance with regulations (such as those governing data privacy and the integrity of financial reports), also performs an analysis to detect and limit fraud and create systems to prevent theft .

Defensive efforts also ensure the integrity of data, which flows through the company’s internal systems, standardization and governance of authorized data sources, such as sensitive customer, vendor, and sales information, in a “single source of truth” called by its acronym in English as SSOT.

The data offense focuses on supporting business goals, such as increasing revenue, profitability, and customer satisfaction. Typically, it includes activities that generate customer information such as data analysis and modeling to support managerial decision-making through, for example, interactive dashboards.

ofense data table
Sometimes it is good to pay equal attention to offense and defense, but it is not wise to predetermine a 50/50 division.

A sound data strategy requires that the data contained in the SSOT source be of high quality, granular and standardized, and that the multiple truth sources be carefully controlled, derived from the same SSOT. This requires good governance, both data and technology.

Data definitions can be ambiguous and mutable, but without a concrete definition at the beginning of what constitutes “truth” (either an SSOT or MVOT), stakeholders waste time and resources, while trying to manage non-standardized data.

If the rules for adding, integrating, and transforming data are not followed, especially when the transformation has poorly defined steps, it is difficult to replicate the transformations and take advantage of the information across the organization.

Feedback loops, to enhance data transformation, are absent, such as predictive modeling for complex analysis. Without mechanisms to make these results available to others (for example in appropriate MVOTs), stakeholders can unnecessarily duplicate work or miss opportunities.

Sound data governance depends heavily on good technology oversight, and generally involves review meetings composed of business and technology executives.

If technology rules prevent a marketing executive from buying a server on their corporate card, they are much less likely to create unregulated “hidden” MVOTs, or marketing analytics that duplicate an existing one in another area.

The regulated and competitive environment of a company’s industry will inform the data strategy

data strategy graphics

Competing in analytics

Companies looking for high-impact applications or third-generation innovations generally concentrate all their capacity in an area that represents the greatest competitive advantage. But a new class of companies is being born and breaking with these schemes.

Organizations like Amazon, Harrah’s, Capital One and the Boston Red Sox have excelled in their fields by implementing industrial strength analysis in a wide variety of activities.

In essence, they are transforming their organizations into high-impact application armies, thus paving the way for victory.

Today’s businesses are saturated with data, forcing organizations to compete with their analyzes. It is now when companies offer similar products, using comparable technologies, causing business processes to be at the last points of differentiation and analytical competitors to squeeze every last drop of value out of these processes.

Therefore, you need to know what products customers want, the prices they are willing to pay, how many items they will buy in life, and what triggers motivate them to buy more.

Employees hired for their experience with numbers, or trained to recognize their importance, are armed with the best evidence and the best quantitative tools, making it easy to make the best decisions.

In traditional companies, departments manage analytics while calculation functions select their own tools and train their people. It is in this way that chaos knocks on the door.

Analytical competitors understand that most business functions, even those such as marketing, can be enhanced with sophisticated quantitative techniques.

By doing this, organizations do not gain the advantage of an innovative application, but of multiple applications that support many parts of the business, and in some cases, are implemented to be used by customers and suppliers.

Table of customer and supplier applications

You compete in analytics when:

  1. You apply sophisticated information systems and rigorous analysis with a range of functions as varied as marketing and human resources.
  2. Your executive team recognizes the importance of analytical skills and focuses on its development and maintenance.
  3. You base decision-making on facts and make this practice part of a constant internal culture.
  4. You consider key to success, hiring not just people with analytical skills, but many people with the best analytical skills.
  5. You use analysis in almost all functions and departments, you also manage it at the strategic business level.
  6. You invent your own metrics to be used in key business processes.
  7. You not only use data in abundance and internal analysis, but also share it with customers and suppliers.
  8. Take every opportunity to generate information, creating a culture of “test and learn” based on numerous small experiments.
  9. You develop entrepreneurial skills for several years to compete proficiently in analytics.
  10. You make quantitative capabilities part of your company’s skills, emphasizing the importance of internal analytics and annual financial reporting.

The most competent analytical professionals not only measure their own navels, they also help customers and suppliers measure theirs.

In order to compete in analytics, it is convenient to educate employees to base their decisions on concrete facts and to know that their performance is measured in the same way. Senior executives also set an example with their own behavior, exhibiting confidence in facts and analysis. Finally, the data collection strategy must be supported by the steps and aspects previously stated.