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The perils of ignoring software development

February 2015 | byPeter Andén, Chandra Gnanasambandam, and Tobias Strålin  Software is a key to market differentiation and value creation for an increasing number of products and services.   http://www.mckinsey.com/insights/high_tech_telecoms_internet/the_perils_of_ignoring_software_development?cid=other-eml-alt-mkq-mck-oth-1502 As digital technologies relentlessly reshape competition, products and services increasingly depend on software for differentiation and performance. Software is behind smartphones and other interfaces that guide consumer interactions; algorithms orchestrate productivity-boosting process automation; wearable devices loaded with software monitor the health and performance of athletes and patients alike. Despite the mission-critical nature of software, it gets surprisingly little attention in the C-suite. Most often, it is relegated to functional managers, several levels down the organization, who manage teams of programmers. New research suggests, however, that companies pay a price when they undervalue the strategic importance of producing excellent software. We examined three core measures of software-development performance at 1,300 companies of varying sizes and across all regions of the world.1 We found not only stunning differences between the highest- and lowest-performing organizations but also sizable differences between the top and average performers (exhibit). Top-quartile companies developed software upward of three times more productively than companies in the bottom quartile. They had 80 percent fewer residual design defects in their software output. Our research also shows that the companies benefited from a 70 percent shorter time to market for new software products and features. This performance gap means that top companies can speed up the flow of new products and applications at much lower cost and with markedly fewer glitches than other companies can.

Exhibit

Software-development performance varies significantly across development groups and companies

The coming revolution

Such performance leverage will become even more important as the transition from hardware- to software-enabled products accelerates. Today’s shift resembles what occurred in the 1970s, when digital electronics began replacing the mechanical and analog technologies that underlay products from calculators to TV sets. The number of top 100 product and service companies that are software dependent has doubled, to nearly 40 percent, over the last 20 years. Value is shifting rapidly as hardware features are increasingly commoditized and software differentiates high- from low-end products. And ever more miniaturized computing power means that the value of embedded software in products is expected to go on growing. Already, software enables an estimated 80 percent of automobile innovation, from entertainment to crash-avoidance systems, according to automotive-software expert Manfred Broy (an electric vehicle may have 10 million lines of code, and a typical high-end car can have many times that).2 Interfaces will become even more sophisticated—and critical—as a growing variety of products, from home appliances to mobile medical devices, are designed around smart screens. As software-enabled customer interactions become the rule, revenues from digitized products and channels are expected to exceed 40 percent in industries such as insurance, retailing, and logistics. The software-led automation of manufacturing and services has generated rising output while reducing costs. And companies with consistently high-performing software experience less operational downtime and develop products with fewer glitches that mar the consumer experience. In a recent letter to shareholders, General Electric CEO Jeffrey R. Immelt offered a view of where things are headed: “We believe that every industrial company will become a software company.”3

Raising the profile of software development

CEOs need to determine whether they have the right organization and capabilities to compete in an environment where software continues to change the game. Asking three questions can help start the process: What are the strategic stakes? CEOs and their top teams should quickly get up to speed on how software could be differentiating or disrupting their current businesses and industries. Scania creates a competitive edge for its trucks through advanced software features that give drivers real-time information on how to optimize fuel use and maximize safety. Semiconductor maker MediaTek invested in software-based reference designs4 in the wireless chips it produces for smartphone manufacturers. The new offerings upended competition in the high-volume, low-end smartphone industry, leading to a tenfold increase in MediaTek’s sales of wireless chips within a single year, as customers benefited from lower development costs, faster times to market, and increased design flexibility. Where does our software power reside? Outside the technology sector, senior software leaders are rarely in the top-management hierarchy. Many companies manage software strategy three to five levels down in the organization, within scattered departments often dedicated to designing and building hardware platforms. Siloed software expertise makes it difficult to assemble a strategic core of software leaders who can think cross-functionally about innovation or productivity. One path forward is to give a software-development executive a seat at the top-management table. Companies can do so by establishing an office—chief of software development—that reports to the CEO, much as companies have done in recent years with the role of chief digital officer or chief information-security officer. Such an executive is well positioned to help high-ranking executives understand how the software-development performance of their company stacks up against that of its peers, the risks of substandard processes, and the strategic importance of improving software-development performance by overhauling organizational structures, development methods, and metrics.5 How do we build the required software-development muscle? In many industries (again, apart from high tech), hardware and mechanical engineers dominate the engineering leadership, so it is difficult to attract the talent needed for cutting-edge software R&D teams. Companies can break through in two ways. The first is mounting an effort to change the organization, developer by developer: building a software powerhouse organically, from existing internal organizations, while targeting top software companies to get strong contributors, who will become software champions and talent magnets. A second option is acquiring a software company to break into new technology areas and get a higher level of software capability. Walmart followed this approach, acquiring a number of smaller start-ups to strengthen its position in e-commerce as well as social and mobile retailing. In either approach, companies need to follow through with software-friendly operating models that incorporate agile working methods, flexible hours, and motivational tactics (such as internal competitions) that spur developers to engage with innovative and challenging projects. Unconventional hiring processes (coding contests or testing online gaming skills, for example) may be needed to screen candidates and identify top talent—as some top digital players already do. There’s no escaping the competitiveness of today’s software-talent marketplace, which is particularly challenging for large companies seeking to build their capabilities. As digital technologies continue reshaping markets, though, there’s little alternative. Embracing the rising strategic importance of software, and viewing its development as a crucial competitive battlefield, are keys to success for an ever-growing number of companies.

About the authors

Peter Andén is an associate principal in McKinsey’s Stockholm office, Chandra Gnanasambandam is a principal in the Silicon Valley office, and Tobias Strålin is a principal in the Seattle office. The authors wish to thank Karim Doulaki, Simone Ferraresi, and Shannon Johnston for their contributions to this article.

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Daniel Levitin: “The Organized Mind: Thinking Straight in an Age of Information Overload”

The information age is drowning us with an unprecedented deluge of data. At the same time, we’re expected to make more—and faster—decisions about our lives than ever before. No wonder, then, that the average American reports frequently losing car keys or reading glasses, missing appointments, and feeling worn out by the effort required just to keep up.

But somehow some people become quite accomplished at managing information flow. In The Organized Mind, Daniel J. Levitin, PhD, uses the latest brain science to demonstrate how those people excel—and how readers can use their methods to regain a sense of mastery over the way they organize their homes, workplaces, and time.

WATCH 

4 Key Big Data Jobs and How Much They Pay

http://smartdatacollective.com/bernardmarr/293236/revealed-4-key-big-data-jobs-and-how-much-they-pay

There are lots of roles that involve working with analytics and big data, and people doing these jobs give themselves many different names.

If you want to be employed working with data, searching the job sites will yield many possible roles – data engineer, information architect, data analyst – and the distinction is not always clear.

So here’s a selection of some of the most commonly used, as well as (in my experience) what they are likely to spend their time doing!

Data Analyst

Probably the most common title, though the responsibilities, salary and requirements can vary significantly. This person could find themselves part of a large team or, with a smaller company, taking sole responsibility for generating data-driven insights.

Most vacancies require a degree in mathematics, statistics or computing as well as relevant experience (up to several years for more highly paid positions). Common skills that you will be asked for include SQL, R, SAS and Excel, and often Hadoop, particularly if the job is geared to big data (as they increasingly are).

According to Payscale.com, in the United States the salary for roles with this title ranges from $36,139 to $77,696, with the median at $51,224. Having skills in statistical analytics, data modeling and SAS in your portfolio will push you towards the top end of that pay scale.

Data Scientist

Again, this could vary wildly depending on the organization, but a data scientist is more likely to work at the strategic side of a data-driven initiative, rather than the operational.

While, as the name implies, an analyst will spend a lot of their time combing the results of the analysis for insights, a data scientist is likely to spend time creating the algorithms that will produce the results. As the rather grandiose name implies, they are a scientist – which essentially means they will come up with theories, and run experiments in order to test them.

People with the job title data scientist tend to attract a higher salary than those titled data analyst, reflecting that this is often a more senior position within an organization, or that more experience is often required for these roles.

As well as the analytics skills that an analyst will be expected to have, data scientists will often be expected to have programming skills such as Java or Python and knowledge of machine learning.

They can expect to earn salaries between $63,192 and $142,118 in the US, with a median salary of $96,579.

Data Architect

A data architect is usually specifically responsible for designing the way a company or organization’s information will be stored. Relational databases, data warehouses and distributed storage systems are the tools of the trade. They may also be responsible to some extent for verifying the data and complying with regulation. However as with all these roles there are massive differences between companies and many data architects will also be involved with analytical projects.

Successful applicants for jobs with this title usually have a degree in computer science and expertise in databases. Those with knowledge of OracleDB are likely to out-earn those with Microsoft SQL experience and the salary ranges from $65,928 to $157,868 in the US, with a median of $105,581.

Chief Information Officer

The CIO is a senior business executive who takes overall responsibility for the data strategy. They are also often the senior member of staff overseeing all technical aspects of a company from basic IT infrastructure to big data analysis, though large firms may have a chief information officer and a chief technology officer. They will generally be in charge of a large team, with senior data scientists and analysts reporting directly to them, while they in turn report to the board of directors.

A variation to this job is the Chief Data Officer, which I come across more and more. It is basically a strategic c-level job, but with a particular focus on data.

Their vision and expertise will play a key part in driving the business, and they will have been hand-picked for this role by the board. They will also be responsible for setting IT policies and ensuring compliancy with laws and regulations.

Salaries at US firms for people with this job title range from $81,226 to $269,033 with a median of $142,269.

There are many other titles you may come across – I have seen vacancies for data engineers, insight directors, big data analysts, principle analysts, data expert, chief data officer – but I believe they all (broadly) fall into one of the categories I covered above.

There is a lot of overlap between these roles, as individual companies will all have their own ideas about how to distribute their workload between their staff. And in particular the titles data analyst and data scientist are often used as catch-alls, but I expect the distinctions will become more defined as data strategies become more widely adopted by business.

As always, I hope you found this post useful and I am always keen to get your comments and views.

You might also be interested in my new book: Big Data: Using Smart Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance