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Doing Big Business With Big Data? Avoid These Big Dangers

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StrategyDriven Organizational Performance Measures Article
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Big Data is that important that businesses are doing more than using it to increase traffic. Today, savvy enterprises realise there is money to make, and they are trying to monetise the information they have. In fact, this blog has a post about why a business needs to start monetising data.

There is no doubt that companies can make a lot of money from information, as seen by Big Data’s rise in popularity. However, just because there is an opportunity for revenue doesn’t mean it is worth taking. Before you attempt to get into the Big Data world, you should understand the big pitfalls.

Here are the ones to watch out for regarding monetising info.

Ownership Rights

Don’t make the mistake of assuming the data is yours to sell in the first place. It is important to remember that the company might not have the rights depending on the chain of events. For example, you might not have asked customers to accept the terms and conditions when they landed on the site. Or, the info might have come from a third party which muddies the waters. Before any data goes on sale, you need to have the right of ownership. Otherwise, they could be a lawsuit in the firm’s near future.

Contracts And Privacy Policies

Understand that the contracts and policies which apply now might be void in the future. It is possible for a stipulation to exist which terminates the security protocols in place and leaves the firm vulnerable. At the very least, a disclosure will be necessary to cover all of the bases. As a rule, take a look at the policies which relate to data and double check the fine print. It is better to be safe than sorry.

Laws And Regulations

The government takes the transference of data seriously, particularly in the day and age of extremist terrorism. Therefore, they pass laws which prevent the sale or transmission of certain pieces of information. If you are in possession of such a file and don’t comply, the consequences will be severe. Depending on who the info goes to, it could be treason. The way to stay on the right side of the law is to research ITAR compliance and EAR compliance. These are the regulations that deal with data transference.

Probably the biggest issue with monetising data is confusion. Because there is a lot to handle, it is easy to mess up the collection and storage processes. Not only does this affect the money side of things, but it is also a security flaw. Big data monetisers, to avoid this problem, form different organisations to specialise in this area. Although it seems like a big move, it is a clever and hassle-free way to cash in on data. Keeping the two sides separate negates confusion and smoothes out the business side of things.

There are lots of opportunities with Big Data, but there are lots of dangers, too.

Surf your data!

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Is your strategy built on received wisdom or analysis of performance data? – management rhetoric or business reality?

Are you building your business strategy on received wisdom or real data? Corporate strategies are often based on assumptions about what drives business performance rather than data from the company itself. J.W. Marriott (founder of Marriott Hotels) is famous for saying “You’ve got to make your employees happy. If the employees are happy, they are going to make the customers happy”. TNT Express promotes the slogan “Take care of your people, let them take care of your customers and the rest will take care of itself”. The implication is that happy employees make happy customers, which drive profits. But does this really happen in your organisation?

The problem is that often some drivers of performance aren’t measured at all; let alone the correlations between them. For example, you may believe that loyal employees create satisfied, loyal customers, but do you have data which demonstrates that your longest serving staff create the highest levels of customer loyalty? Another assumption is that loyal customers are the most profitable; we’re often told ‘it is five times more profitable to serve existing customers than loyal customers’. It makes sense. The better we know our customers the better we are likely to serve them. And because customer spend tends to increase over time, it may well be cheaper to serve long-term customers than keep attracting new ones. But, can you prove this is the case in your organisation?

Performance topology mapping is a tool that can help with this analysis. The first step is making sure that you’re measuring the right thing. So if your business is built on the assumption that employee loyalty is necessary to create loyal customers, collect loyalty data. Identify your key performance indicators, and then measure the correlations between them in order to build a map of business drivers.

The findings can be astonishing. For example, the link between customer loyalty and financial performance is often regarded as a basic principle of retail management. However when they came to explore the data in their own organisation, the management of one home improvement retail chain discovered that there was no such correlation. They could not prove that the stores with the most loyal customers were the most profitable.

Analysis of the performance topology map of one of the UK’s big four grocery superstore chains also revealed counter-intuitive results. Its management bought into the idea that satisfied employees created customer satisfaction which drove store profitability. But the data revealed negative correlations! In fact the stores with the highest levels of employee satisfaction were the least profitable. The explanation for this lay in the value proposition: customers in these stores did not value contact with staff so much as product availability, price and checkout speed. Therefore their shopping experience did not hinge on the quality of their interaction with employees.

In other businesses, of course, the interactions between staff and customers are likely to be much more critical. Take, for example, the professional services of clinicians or lawyers. Their services are based on more sophisticated interactions between staff and clients, and long-term business relationships may well be an essential part of the value proposition. Therefore employee engagement is likely to be a more important driver of profitability in professional services.

Understanding the performance drivers is crucial. Because failing to understand what drives profitability is to fail to understand why your company has succeeded… or indeed failed. The reality is that your business strategy is based on all sorts of assumptions about what investments will yield increased market share, revenue growth or profitability. To get the strategy right, better start testing those assumptions… surf the data wave!

About the Author

Dr. Rhian SilvestroDr. Rhian Silvestro is Associate Professor of Operations Management at Warwick Business School. Rhian has conducted service management research in a number of large, leading edge organisations including retail companies, banks, transport companies, health services and call centres. She has publications in over ten international journals in the fields of service design, performance improvement and supply chain integration.

Do You and Your Organization Speak Data?

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StrategyDriven Organizational Performance Measures ArticleSpeaking two languages makes you bilingual, and speaking three makes you trilingual. Any more than that, and you are a polyglot. In today’s data-driven business world, you are a data scientist if you can “speak data”.

Our world is becoming more and more about the data it generates. As pressure mounts, people who can analyze, visualize, and interpret data are becoming indispensable, much like a well-versed polyglot who can interpret and translate multiple languages with ease.

Speaking the language of data

Data surrounds us, and the ability to understand and interpret it should be a natural requirement for every individual and organization. Perhaps data and its projection on every surface of our surroundings will be the world’s new sign language. Thus, the new generation of human capital must possess this fundamental skill.

As individuals, we are challenged by the overwhelming amount of data we interact with in every scope of our lives. Learning how to make sense of data is becoming a necessity rather than a choice. If we want to continue to be part of this fascinating and engaging ecology – the world of Big Data, including the smart appliances, classrooms, schools, workplaces, and cities we anticipate in the near future – we need to be able to go beyond just speaking the language of data.

Using a data-driven strategy as a competitive advantage

It does not take a sophisticated algorithm to see the value of data scientists on today’s organizations. Clear distinctions are emerging between organizations that embody and embrace the data-driven world we live in and those who have not adapted and are still following a traditional approaches. Competitive organizations are embracing big data and re-engineering their strategies and processes accordingly.

In essence, these organizations are expanding their family of employees who are well-versed in data at every level of their managerial hierarchy. Clarity and transparency are of the utmost importance to data-driven environments where everyone speaks the language of data.

First and foremost, organizations have limited choices in today’s extremely dynamic business world. Data-driven strategies are inherently dynamic strategies that can help organizations bring the necessary transformations based on materialized and projected evidences. Data-driven strategies are also inherently granular, allowing management to sync and assess different layers of decisions and actions. Furthermore, data-driven strategies permit clear communication, responsibilities, and accountabilities at various decision layers.

Creating a data-driven culture

More importantly, the benefit of speaking the language of data allows organizations to be active in their communities and to learn through continuous engagement and feedback from their stakeholders. These are realities no organization can ignore for survival. However, in order to be competitive, organizations need to delve into the nitty-gritty of the language of data: the grammar, punctuation, and spelling that are required to be proficient in the world of big data. It not only requires passion, but also a bit of obsession.

Eloquent data speakers such as Google, Facebook, and Amazon serve as great role models for other organizations that are encouraged by the returns they see and that understand the growing need for their employees to communicate through data. This shift is not limited to creating a subset of employees who can analyze data, but to create a data-driven culture and environment that embraces all employees’ internal and external interactions as members of the big data ecology.

About the Author

Anteneh Ayanso is an Associate Professor of Information Systems at Brock University’s Goodman School of Business. He is certified in Production and Inventory Management (CPIM) by APICS and teaches and researches in the areas of data management, business analytics, electronic commerce, and electronic government. Anteneh Ayanso can be contacted at (905) 688-5550 x 3498 or [email protected]

5 Ways Predictive Analytics Make or Break a CRM System

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Size doesn’t matter: why predictive analytics prove your customer relationship management methods are likely falling short

 
For a sales-driven organization, it isn’t the size of your data that matters, it’s what you do with it. No longer a discretionary luxury, predictive analytics are now the name of the game for those who seek to utilize customer metrics in a meaningful way to establish a tremendous competitive advantage, gain notable market share and significantly boost bottom lines. In fact, according to the 2015 State of Sales Report published by Salesforce Research, “smart selling fueled by predictive analysis is expected to jump 77% among high performers,” throughout 2016. Not only that but high performers are also four times more likely to use predictive analytics.

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About the Author

Lang SmithLang Smith is the founder of Cloud Signalytics – a first-of-its-kind predictive intelligence software platform helping major franchise auto dealerships create highly precise, individualized customer profiles to maximize sales. He may be reached online at www.cloudsignalytics.com.

Source: https://secure2.sfdcstatic.com/assets/pdf/misc/state-of-sales-report-salesforce.pdf