{"id":988,"date":"2019-11-07T14:55:07","date_gmt":"2019-11-07T14:55:07","guid":{"rendered":"http:\/\/janbosch.com\/blog\/?p=988"},"modified":"2019-11-07T14:55:16","modified_gmt":"2019-11-07T14:55:16","slug":"what-use-is-your-data","status":"publish","type":"post","link":"https:\/\/janbosch.com\/blog\/index.php\/2019\/11\/07\/what-use-is-your-data\/","title":{"rendered":"What use is your data?"},"content":{"rendered":"\n
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Photo by Nick Hillier on Unsplash<\/figcaption><\/figure>\n\n\n\n

If you\u2019ve been reading my posts, you know that I feel data is one of the key ingredients of a successful digital transformation. It\u2019s not just about adding software to your products or putting DevOps in place. It is as much about collecting, analyzing and storing data and using this data to improve a variety of aspects of the business. As, in my experience, every proponent of the use of data has a specific and different interest from others, the interesting question is what these relevant aspects are. Here, I provide an initial taxonomy of the uses of data.<\/p>\n\n\n\n

At a top level, we can distinguish between the use of data \ninside and outside the company. Internal use can often be broken down \ninto the use inside the R&D organization and the use in the rest of \nthe company. Inside the R&D organization, data has traditionally \nbeen used for quality assurance of products out in the field. During \nrecent years, at least three purposes have been added to that: \nanalytics, experimentation and artificial intelligence\/machine \nlearning\/deep learning.<\/p>\n\n\n\n

Inside R&D<\/h2>\n\n\n\n

Analytics<\/strong><\/p>\n\n\n\n

The\n first is the use of data for analytics purposes. This includes gaining \nan understanding of the number of users, the frequency of use of \ndifferent features and user experience issues such as aborted actions. \nIn this case, data is used to determine whether our model of the \ncustomer and the system in the field is aligned with reality.<\/p>\n\n\n\n

Experimentation<\/strong><\/p>\n\n\n\n

The\n second use of data inside the R&D organization is concerned with \nexperimentation. In those areas where the analytics show that our \nunderstanding of the customer or the deployed system is lacking, teams \ncan use experimentation, such as A\/B testing, to try out in the field \nwhat the best way for realizing features and system functions is. The \nbest way to perform this from a statistical perspective is to run fully \nrandomized experiments, but in practice, also less rigid experimentation\n can generate valuable insights.<\/p>\n\n\n\n

AI\/ML\/DL<\/strong><\/p>\n\n\n\n

The\n obvious use of data that has gained enormous popularity in recent years\n is the use for training machine learning and deep learning models. Due \nto the availability of data and significant improvements in the \nperformance of hardware architectures such as GPUs, DSPs and ASICs, the \nuse of artificial intelligence has become a highly beneficial option for\n many application domains. However, ML\/DL models are very data hungry \nand often need large amounts of (labeled) data for training.<\/p>\n\n\n\n

Outside R&D<\/h2>\n\n\n\n

Moving\n to the use of data inside the company, outside of R&D, the primary \nfocus is often on the financial impact of decisions \u2013 in the entire \norganization. Typical goals include value modeling, tracking performance\n of teams and, again, experimentation and AI\/ML\/DL.<\/p>\n\n\n\n

Value modeling<\/strong><\/p>\n\n\n\n

One\n of the key challenges in companies is to align activities at all levels\n in the organization. One of the most effective approaches is to build a\n hierarchical value model where the top-level business KPIs are \ntranslated into lower-level metrics with quantitatively defined \nrelationships between them. This value model then uses any data from the\n field or from customers to infer other factors.<\/p>\n\n\n\n

Tracking performance of teams<\/strong><\/p>\n\n\n\n

Many\n companies also use data to track team performance in sales, customer \nsupport and other functions. Although this isn\u2019t a new concept, \ndigitalization allows for the use of much more data, as well as \ndifferent types of data. This gives more precise and detailed insight \ninto teams.<\/p>\n\n\n\n

Experimentation<\/strong><\/p>\n\n\n\n

Most\n people assume that A\/B testing was invented by the SaaS companies, \nwhile in fact, it\u2019s a technique originating from marketing in the \nmid-20th century. So, data-driven experimentation can be used across the\n company and many digital companies do apply these principles also \noutside of R&D.<\/p>\n\n\n\n

AI\/ML\/DL<\/strong><\/p>\n\n\n\n

Similar\n to experimentation, the use of ML\/DL models is applicable outside the \nrealm of products as well. For example, in customer support, such models\n can be used to more rapidly help a customer resolve a problem by \nclassifying symptoms into likely issues and, of course, to recommend the\n most likely cross- and up-sell suggestions.<\/p>\n\n\n\n

Outside the company<\/h2>\n\n\n\n

Shifting\n perspective from inside to outside the company, the use of data can be \nseparated into two main areas: providing data-driven solutions to your \nexisting business ecosystem, such as your customer base, suppliers and \npartners, and monetizing data from your primary customer base with a \nsecondary customer base. For the former, some typical uses of data \ninclude preventive maintenance services to customers, business \nperformance analytics and alternative business models.<\/p>\n\n\n\n

Preventive maintenance services to customers<\/strong><\/p>\n\n\n\n

The\n most obvious case that has received lots of attention in the IoT \ncommunities is the notion of preventive maintenance. By measuring \nsystems during operation and having data from many system instances, the\n company can detect likely component failures before they happen and \nthen recommend maintenance at planned downtime periods rather than \nsuffer from breakdowns.<\/p>\n\n\n\n

Business performance analytics<\/strong><\/p>\n\n\n\n

As\n the company has multiple customers and it receives data from all of \nthese, it can provide a service that allows each customer to compare its\n performance to aggregated and anonymized data from others like it. This\n even makes it possible to offer consultancy services to help customers \nimprove their business performance.<\/p>\n\n\n\n

Alternative business models<\/strong><\/p>\n\n\n\n

As\n the company can now measure the value it delivers to customers, it can \nuse alternative business models based on value-based pricing. This often\n includes forms of continuous improvement where the company delivers \nsolutions that improve the customer\u2019s business performance and is \nreimbursed a part of the value created for the customer.<\/p>\n\n\n\n

The final\n category is concerned with monetizing data from the primary customer \nbase with a second customer base. The mechanism of a two-sided market is\n difficult to put in place but the benefits are enormous as it often \nallows the company to use the funds from its second customer base to \nsubsidize its primary customer base and through that, increase its \nmarket share. There are two broad categories of monetization with \nsecondary customer bases: aggregate activity data and customer profiles.<\/p>\n\n\n\n

Aggregate activity data<\/strong><\/p>\n\n\n\n

The\n activity from the entire customer base or a part of it can be \naggregated and offered to others that can use this data to improve their\n value offering to their customers. As a hypothetical example, a company\n selling connected trucks could calculate the total kilometers driven in\n aggregate across the entire customer base every week in every country \nin Europe and offer this information to companies that sell economic \nactivity data. As the amount of goods transported is a good indicator of\n the amount of economic activity in a country, this could be valuable \nfor previously unrelated companies.<\/p>\n\n\n\n

Customer profiles<\/strong><\/p>\n\n\n\n

Rather\n than selling aggregate data, the company can sell anonymized (or not) \ncustomer profiles that others can use. For instance, many online \nbusinesses sell your profile data to ad networks, allowing these to \nserve more relevant ads to you and through that, improve their \neffectiveness. This category often is criticized by many as they feel \nthat their data is used unfairly. However, it has to be obvious that if \nyou get to use an online service for free, the data trail you generate \nis used to generate revenue from other parties.<\/p>\n\n\n\n

Concluding, many agree that collecting and using data is an integral part of a digitalization strategy. The challenge is that they have one specific use in mind and don\u2019t think in terms of a holistic view of the types of data and the possible uses of that data. My goal was to outline a first, high-level taxonomy of the use of data. What data does your company generate and collect and how can you use this data to the advantage of you and your customers? What use is your data?<\/p>\n\n\n\n

To get more insights earlier, sign up for my newsletter at<\/em>jan@janbosch.com<\/em><\/a> or follow me on<\/em> janbosch.com\/blog<\/em><\/a>, LinkedIn (<\/em>linkedin.com\/in\/janbosch<\/em><\/a>) or Twitter (<\/em>@JanBosch<\/em><\/a>).<\/em><\/p>\n","protected":false},"excerpt":{"rendered":"

If you\u2019ve been reading my posts, you know that I feel data is one of the key ingredients of a successful digital transformation. It\u2019s not just about adding software to your products or putting DevOps in place. It is as much about collecting, analyzing and storing data and using this data to improve a variety … Read more<\/a><\/p>\n","protected":false},"author":2,"featured_media":0,"comment_status":"open","ping_status":"open","sticky":false,"template":"","format":"standard","meta":{"generate_page_header":"","footnotes":""},"categories":[15,4,10],"tags":[],"_links":{"self":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/988"}],"collection":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts"}],"about":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/types\/post"}],"author":[{"embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/users\/2"}],"replies":[{"embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/comments?post=988"}],"version-history":[{"count":1,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/988\/revisions"}],"predecessor-version":[{"id":990,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/posts\/988\/revisions\/990"}],"wp:attachment":[{"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/media?parent=988"}],"wp:term":[{"taxonomy":"category","embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/categories?post=988"},{"taxonomy":"post_tag","embeddable":true,"href":"https:\/\/janbosch.com\/blog\/index.php\/wp-json\/wp\/v2\/tags?post=988"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}