This problem can be frequently seen in the retail industry. Company management typically relies on their distributors to collect and feed sales data back to headquarters. A significant amount of time is then spent collating and analysing this data before any meaningful insights can be generated. In fact, we typically see companies reviewing sales records quarterly (or monthly, at best).
Assuming perfect execution of data collation, after taking these quarterly insights and creating a tactical shift for a product, a few additional weeks has passed, such that management needs to make decisions based on only a handful of major data points each year. While better than semi-annually or yearly, it certainly does not hold up in today’s fast- paced business world, where pivots may need to be made on a weekly, daily or even real-time basis.
3. External: inorganic revenue generation
Executives often have hunches and a sense of how to steer their business in a rapidly changing landscape based on their experience. However, while undoubtedly valuable, these perspectives should be verified by data to either strip away personal biases or solidify instincts, forming part of a cohesive business strategy.
A good example of this is a company looking to offer new products through improving its use of data and digital solutions. Take Netflix: while it began as a DVD-by-mail service back in the late 1990s, it pivoted into streaming services in 2007. With this move, Netflix broadened its user base and began collecting a significant amount of information around its viewership habits and preferences.
Netflix subsequently utilised this data generated from its pre-existing business in two primary ways, namely: (1) the identification of new opportunities; and (2) as leverage for studio negotiations. Addressing the first point, Netflix had created customer metrics (i.e. when viewers play, pause, and rewind, when they watch content, device preferences, and location data, etc.) 4 and identified shows and genres that its viewers desired. This provided them with unique insights into targeted licensing from large studios, saving significant amounts of capital (as opposed to the shotgun approach of “license anything we can”). Secondly, its monopoly on viewership data has given Netflix an outsized advantage when negotiating with studios for subsequent licensing or production deals. 5
In summary, Netflix created a new core product (streaming) and fundamentally changed its business model; creating efficiencies around capital allocation (targeted licensing) and yielded tremendous revenue growth. All of this was possible through the company’s devotion to robust data collection and analysis. Additionally, this created a hoard of data for Netflix to utilise which transitioned the company into the summit of any company’s data journey, complete organic revenue generation.
4. External: organic revenue generation
Organic revenue generation occurs when big data begins to develop insights that can directly point a company towards the best way of making more money. In this case, datasets become so large – and analysis so robust – that it begins to develop perspectives from blind spots that manual processing is unable to achieve. As highlighted in Part 1 of our two-part report series, this is quite a rare scenario, as businesses would need to have such an efficient (and effective) end-to-end data value chain that all data is automatically and readily accessible (and usable) to generate insights from novel questions that decision-makers may have.
In the case of Netflix, the company created a recommendation engine that dynamically alters the movies shown on its landing page, tailored to individual user behaviours. This manifested in an attractive user-centric functionality. While a fantastic feat of engineering, Netflix’s recommendation engine is only a front to the true intricacy of the company’s data prowess; the ability to create curated content based on viewership preferences. The sheer amount of data Netflix sits on means that it can generate a profile of a movie or TV show that has yet to be produced but has immense potential with its current audiences.
A prime example of this could be seen with its hit series, ‘House of Cards’, which gave Netflix management the direction they needed with the following viewer insights: (1) David Fincher’s a ‘Social Network’ was frequently watched from start to finish; (2) the British version of ‘House of Cards’ was well received; and (3) viewers who enjoyed these two series also enjoyed Kevin Spacey and/or films directed by David Fincher. 6
Netflix went on to invest USD100 million into the series, which has since been credited with cementing the company’s dominance in the streaming space, with other hit series launched in the same manner. 7
Today, Netflix has a plethora of curated movies and TV series which are not available anywhere else, creating a significant strategic advantage for Netflix and a defensible revenue stream, a clear testament to the power of a tightly managed and holistic data strategy.
Goals dimensions
As highlighted earlier in this report, companies waste significant sums of money in data project investments, largely due to an incomplete understanding of the specific business problems they are trying to address.
There are three key dimensions that firms should consider when deciding what level of investment is necessary, centred around the business use case. These include:
- Time to decision (how fast does the company require information);
- Accuracy of decision (how precise does it need to be?); and
- Volume of decision (how many times does information need to be gathered and how many data points are required?) (see Figure 7).