Getting Big Data talent won’t be easy

 

There’s a knack to finding people to meet your digital challenge

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The ability to collect and interpret data has become critical. The retail and banking sectors have been addressing this challenge for years, and it is a growing priority for consumer and industrial products businesses.

The strategic challenge is broadly the same regardless of sector: data is becoming essential to future success, and there is a real risk of disintermediation – with corporations ending up as commodity suppliers to the big software and data businesses, such as Alphabet and IBM. Whoever controls the data controls the industry.

The obvious way forward is for established multinational corporations to acquire software capability for themselves. But this is not easy, as many are finding out.

The first challenge is that most multinationals simply don’t know how to manage small, high-technology companies. Large global corporations are structured, bureaucratic and well-ordered, with explicit and implicit hierarchies plus well-managed investment allocation and financial reporting processes.

Small, fast-growing software startups are the complete opposite. Software engineers are passionate about their software, rather than the company they work for or the markets they serve. They expect to be treated differently, provided with a flexible working environment and given fast-rising stock options.

This does not fit with the structured and bureaucratic way in which multinationals work, so it is not surprising that the key talent often walks out of the door shortly after an acquisition, leaving the parent company owning a hollow shell of a business with limited value.

The second challenge comes from the multinationals’ shareholders, who are usually more focused on shortterm performance than on longer-term investment. Persuading shareholders to  agree to a high acquisition price, based on a multiple of potential future sales, is challenging, particularly for a loss-making software startup with uncertain prospects. There is a further complication in that software tech companies are often at such an early stage that it is not clear in which market the technology will deliver most value. One aerospace company invested in analytical software for predictive maintenance, only to find that the biggest market for it was in the healthcare sector.

Despite these challenges, it is vital that multinationals take action, as they need to build the software capability to collect and interpret their own data.

Before buying software companies, arm yourself with information. Build an internal team of experts, and hire managers who have experienced both startup and multinational cultures and can act as a bridge between the two. This team can help you through the interim step of buying in software and support services, and also help you adjust your approach to increase the chance of success.

Don’t rush in too early. It is tempting to buy very early-stage companies, as they are comparatively cheap and provide firstmover advantage. All the evidence suggests it is better for large corporations to bide their time, and that post-acquisition integration is more successful where the acquired business already has structure, sales and profits.

Alternatively, with board and shareholder support, you could take a more aggressive stance, investing heavily to buy companies and transform the business. For example, in the 1990s, IBM succeeded in migrating from hardware to software. With GE Digital, GE appears to be doing the same.

For most, the best approach is to build internal competence gradually and then acquire capability. Given this is a slow process, you had better make a start now.

 

Chris Floyd is a Rolls Royce leadership educator

1 thought on “Getting Big Data talent won’t be easy”

  1. Chris – totally agree with your conclusion: “For most, the best approach is to build internal competence gradually and then acquire capability.”

    One of our most popular UC Berkeley CorpEd programs that can be customized for organizations in sectors such as manufacturing, healthcare, financial services, and energy is called: “Introduction to Data Sciences and Analytics”.

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