PERSONAL SPECIAL.... How I Disconnected from the Digital World to Regain Control of My Life

How I Disconnected from the Digital World to Regain Control of My Life

Our smartphones are never far away from our fingertips and in this digital world most of us couldn’t function without them. So how often do you use your phone? How many times during the day do you swipe, use apps, check social media, send messages or even just generally handle your phone?

Well, to really drive home how much we mindlessly touch and use our phones, a recent study has revealed that we do this a whopping 2,617 times a day and that’s just the average – more heavy users can handle their phones up to 5,427 times a day.

How have we become so obsessed with the digital world and is it time to unplug ourselves from the mindlessness it provides us?

Why Is It So Hard To Unplug?

We’re all so dependent on technology that we rarely disconnect. Whether we’re spending hours in front of a computer for work, checking our phones, surfing the internet or watching TV, it’s hard to get away from digital distraction.

You may have attempted to go phone-free or deactivated your Facebook account in hope of a digital detox and we all know it feels good but only for the short-term. Before long we’re itching to see what we’re missing. In other words, we’re addicted. This can manifest in the feelings of withdrawal we get that causes us to dive straight back into the digital world where we feel safe and soothed again.

Many of us feel like our phones are a form of comfort – a lot of our social lives revolve around social media and instant messaging, so without this, we can feel secluded and alone.

The Benefits of Disconnecting from the Digital World

Are we using technology or is technology using us?

Every happiness guru talks about mindfulness as a core importance in being connected with ourselves and the world around us, but our need for constant connection to technology means we’re depriving ourselves of this fundamental and necessary habit.

Our ability to focus has decreased dramatically and this is apparent in our productivity levels. The benefits of disconnecting can create a positive stance in all areas of our lives – from work and social connections to our own personal goals and dreams. If our productivity levels increase, we feel much more fulfilled, content and happy with our abilities. Life becomes more meaningful and less shallow.

How to Disconnect from Technology and Regain Your Life

If you feel your connection to the digital world has taken over your life, there are steps you can take to help you try to disconnect and allow you to take back some power.

1. Create a Technology-Free Space

Move your laptop into a dedicated room, put your phone charger in there so it can’t be charged next to you. When you allocate a certain place for your gadgets, you will have to physically go there to use them and so the inconvenience will lessen your want to go and check them.

2. Don’t Sleep with Your Phone Next to You

Our sleep is being severely disrupted due to blue light being transmitted when using our phones or tablets in the dark. Our brains can’t switch off so easily and so it’s hard to relax and drift off. Put your device at the other side of the room so you can’t check it before bed, during the night or first thing when you wake up.

3. Go off the Grid for One Night a Week

Okay, so we rely heavily on being available to be contacted but for one night a week try switching off your phone, computer and tablet. Tell people they won’t be able to contact you via technology unless it’s an emergency. Don’t check social media or your messages, instead try reading an interesting book, experiment in the kitchen or go for walks.

4. Plan More Non-Digital Activities

Make a conscious effort to plan more activities that don’t include technology to keep yourself distracted. Plan a hike, bike ride, have a hot bubble bath, join a club, go to an exercise class, start a new hobby or take a trip to your local library and set yourself a challenge to read a certain number of books a week.

5. Get Friends to Join You

Persuade a good group of friends to join you in your digital detox. Think of it as a support group – get together and do something that doesn’t involve technology or discuss the benefits you’re all feeling from disconnecting. This will reinforce the positive feelings and progress from going digital-free.

6. Start a Mediation Practice

Mindfulness is probably something you’ve heard a million times but it’s truly important in order to be present in the here and now. Try meditating for just 10 minutes a day and build it up. If you do this first thing in the morning you’ll set your mind up for a good day and you’ll start to see the benefits over time.

7. Be More Aware of Your Surroundings

Continuing the mindfulness theme, try making an effort to be aware of what’s going on around you. That includes sounds, smells, as well as sight. How often do we walk and look at our phones? Put your phone in your pocket and try a bit of mindful walking. Notice how you walk, the feeling, the action, what there is to look at, the sounds you hear – it’s quite shocking how much we don’t pay attention to the wonderful world around us when our nose is planted in our phones.

8. Log out of Social Media

If deactivating your account is too much then consider logging out of social media every time you use it. It’s all too easy to hit the app and you’re instantly looking at your feed but if you have to type in your username and password every time, it’ll not only make you more aware you’re doing it but you’ll also start to see it as a hassle.

9. Disable Phone Notifications

It’s tempting to check our phones every time we get a notification so try turning them off and dedicate a time later to check up on anything important. This will seriously reduce the amount you needlessly check things that probably aren’t even important.

10. Install Social Media Blocking Apps

If you feel you’re one of the addicts who handles their phone 5,427 times a day then consider installing apps that block you from accessing social media apps. Offtime helps you unplug by blocking all the distracting apps and also creates data on how much you actually use your smartphone. Or if it’s your computer that’s stopping you from being productive, then SelfControl for Mac or ColdTurkey for Windows will really help.

We could all do with a bit of digital downtime, if not for our productivity levels then our sense of mental well-being. Be more mindful of how much you use and rely on technology and find little ways of filtering it out, make it a habit and start creating a happier life.

Jenny Marchal is a freelance https://www.lifehack.org/578171/how-disconnect-from-the-digital-world

BOOK SPECIAL .....An Ode to the Thief of Time

BOOK ... An Ode to the Thief of Time

In his new book, Andrew Santella explores procrastination, and why it is that whenever there is a job to do, you can count on someone putting it off.

 Soon: An Overdue History of Procrastination, from Leonardo and Darwin to You and Me

by Andrew Santella, Dey Street Books, 2018

In late 1934, a department store magnate named Edgar Kaufmann engaged Frank Lloyd Wright to design a weekend home in the woods an hour or so southeast of Pittsburgh. It was a huge boon for Wright — his reputation had waned, commissions had dried up in the Depression, and his home and studio were threatened with foreclosure. The architect visited the Kaufmann site, asked for a survey, and then, the story goes, didn’t do a damn thing.

Nine months later, Kaufmann unexpectedly visited Wright’s studio to look at the design for his new home, which, he had been told, was progressing beautifully. Wright reportedly put pencil to paper for the first time. Two hours later, he presented Kaufmann with a plan for Fallingwater, an acknowledged masterpiece of residential architecture.

“The only way to explain the nine months Wright spent not working on Fallingwater is by procrastination’s perverse logic. Nothing was the only thing that could be done in such a situation,” writes Andrew Santella in Soon, his engaging, meandering, and, of course, overdue exploration of the behavioral tic.

Santella, a sophisticated and widely published essayist who also coaches baseball at a high school in Brooklyn, doesn’t claim to know why Wright procrastinated. But he raises a skeptical eyebrow at our tendency to interpret such stories as mysterious workings of genius. “This is something like what I tell my wife when she finds me snoozing on the couch,” he says. “I may look like I’m taking a nap, but I’m really writing. I’m always writing.”

It’s clear to me that Santella knows the vagaries of procrastination firsthand, because I, too, am always writing. Procrastination is so ingrained in me that to deny it or to seek to exorcise it feels, to cite a faddish leadership term, inauthentic. And I appreciate Santella’s refusal to try to cure me of my tic as much as I support his disinclination to romanticize it.

Perhaps there is no cure. As Soon shows, procrastination has survived centuries of attack from a variety of institutions of social control, including corporations and churches. In Armenia, in the fourth century, a Roman centurion who had decided to give up his pagan ways and become a Christian met a talking crow. The crow suggested the centurion take another day to think about it. “Realizing that the crow was, in fact, the Devil in avian form arrived to tempt him, the centurion — who would later be venerated as St. Expedite, patron saint of procrastinators — did something remarkable,” writes Santella. “He stomped the talking bird to death.”

A millennium later, Italian business owners installed clock towers to regulate their employees. “This is also when clocks began to tell us what we were worth,” explains Santella. “From the beginning, these rising towers enabled a new attitude toward time and the need to deploy it wisely.”

Fast-forward 500 years, and we encounter Frederick Winslow Taylor, the patron scourge of procrastinators, who tried to eradicate our behavioral tic with the invention of scientific management. Taylor, who was sarcastically nicknamed “Speedy” by the workers at the Watertown Arsenal in Massachusetts, noticed that industrial workforces were unable to move faster than their slowest worker. He called this “soldiering” and dedicated himself to wiping it out. “Soldiering is related to procrastination, in that the soldierer sabotages the efforts of the collective the way a procrastinator frustrates himself,” writes Santella.

Jump another 100 years and behold the era of the gig worker. “Now, entire sections of Brooklyn and Chicago and Portland and Austin are populated almost entirely by loitering freelancers — which is to say procrastinators,” observes Santella. “The blithe dereliction characteristic of our contract economy has helped normalize procrastination.” But Speedy must be spinning swiftly in his grave.

Perhaps because writers tend to work alone, Santella doesn’t spend much time exploring the effect of our procrastination on others. I suggest that responsible procrastinators should try to avoid inflicting the wages of their sin on other people. But I also know that irresponsible procrastinators wreak havoc on schedules and drive their bosses and colleagues to distraction.

That is the best reason for executives, who have likely spent their careers completing to-do lists early and asking, “More, please,” to read Soon. Better a devil you know than one you don’t.

by Theodore Kinni

https://www.strategy-business.com/article/An-Ode-to-the-Thief-of-Time?gko=34ea2&utm_source=itw&utm_medium=itw20180515&utm_campaign=resp

MANAGEMENT SPECIAL ...Tech Deals Bring New Challenges to M&A

Tech Deals Bring New Challenges to M&A

For established firms, acquiring a digital startup comes with promise and pitfalls.

General Motors, which will be 110 years old in 2018, has made several acquisitions as part of its move to develop a fleet of self-driving cars. In March 2016, it purchased San Francisco–based autonomous vehicle technology startup Cruise Automation, which now runs GM’s self-driving unit. Then in October 2017, GM bought Strobe, a three-year-old startup based in Pasadena, Calif., specializing in lidar sensors that can generate high-definition images — a key component of autonomous vehicles’ navigation systems. Walmart, founded in 1962, has also moved into the tech space to facilitate its creation of a digital marketplace that goes beyond merely selling its inventory online. During the last couple of years, the company has acquired a number of digital fashion retailers, including Jet.com, Bonobos, Modcloth, and Moosejaw, to enhance its e-commerce presence.

Many companies that existed decades before the Internet, and that did not have technology at the center of their business model, are trying to lock in future growth by relying more on the prowess of young tech firms. We reported two years ago on the rise of acquisitions of digital firms by companies not usually viewed as being in the tech sector, noting that such deals had grown by 48 percent between 2011 and 2015. In 2016, tech deals totaled more than US$125 billion — up from $20 billion in 2011 — and that momentum carried into 2017. Through August of that year, 51 percent of investments in private tech companies came from non-tech corporations, according to CB Insights.

But before jumping in, traditional firms need to consider the unique characteristics and challenges of tech deals. This will require them to think strategically about their own digital agenda and how best to achieve it; to consider the various factors that can influence the terms of a deal, including cyber-attacks; and to be mindful of the skills and mind-sets that the digital firm’s technologists and leaders may bring to their new organization.

Making the Right Purchase

Non-tech companies acquire tech startups for different reasons. They may want to enhance their business or provide entirely new digitally enabled services. They may also want to preempt disruption and buy out a company they view as a potential competitor. The trouble is that deal makers are evaluating technologies that have only begun to emerge; they thus have limited history and information to go on. Moreover, even though established firms may have created a blueprint for how new technologies could enhance their business, most of these plans are relatively new and still evolving.

In the past, companies would factor such risks into the price or terms of the deal. Although that continues to happen, one alternative that’s become increasingly common in recent years is for firms to establish a partnership or joint venture (JV) rather than make an outright acquisition. Such agreements can decrease the risks involved in acquiring technologies that are relatively new. What’s more, partnerships and JVs can address some of the common challenges of a traditional M&A, including bearing financial debts or sharing sensitive data, and can serve as a testing ground for companies that are intrigued by new technologies but aren’t ready to commit. In fact, PwC’s 21st CEO Survey, which involved nearly 1,300 executives around the world, revealed that the trend toward partnerships is occurring across all types of M&A. About half of CEOs said they planned to pursue a new strategic alliance or JV to drive corporate growth and profitability in 2018, compared with four out of 10 executives who said they planned to pursue new M&A.

With partnerships, the two sides benefit in different ways: The non-tech firm gains exposure to expertise and technology that it lacks; the tech target keeps its independence and benefits from the vast resources that took the non-tech acquirer years to build, including capital and decades’ worth of commercial data. Those who see more to the partnership may eventually choose to strike an M&A deal, which is not uncommon, as we saw in September 2017 when furniture company IKEA acquired tech startup TaskRabbit. The deal emerged after the two companies piloted a furniture assembly partnership in the United Kingdom.

It’s also worth noting that established companies need to take a rigorous look at their digital strategies before initiating tech deals. Many incumbents have developed such strategies, outlining how they plan to grow by adopting new technologies, but one lingering challenge is that many of these strategies are mostly experimental and need time to evolve and mature. Potential acquirers need to ask themselves whether buying this company makes sense for their business, and where they want to go with the acquisition.

Striking the Right Price

High-priced acquisitions have been especially prevalent in tech deals, as more startups reach “unicorn” valuations of $1 billion or more. Deal makers kicked off 2018 with a string of megadeals north of $5 billion. But not overpaying on deals involving tech startups remains a challenge. A common refrain is that deal makers should set a walk-away price early on and avoid bidding wars. Many investors make the mistake of buying into the target’s growth story in ways they wouldn’t for more traditional acquisitions. For instance, they may place a bigger emphasis on the technological expertise of the target firm than on how much revenue it expects to generate. This is especially true for early-stage tech targets, which are often quick to put new technologies to use but have yet to demonstrate profitability.

When evaluating early-stage tech companies, it’s particularly important for the acquirer to question the assumptions underlying the tech target’s projections for growth, including assumptions involving user adoption and penetration rates among consumer-facing technology companies. The acquiring company should also examine the target firm’s products — including the scalability of its technology and how it plans to enhance and improve its technological offerings. And because it’s not uncommon for established firms (which typically have more to spend on R&D and other investments) to pay more for a tech acquisition than a startup would, it’s important to bear in mind that a target’s price varies depending on who the bidder is.

Another factor to consider is cybersecurity. It’s still rare for a breach to kill a deal, but it could delay the transaction or impact a target’s value. That was evident in 2017, when Verizon acquired Yahoo. After Yahoo’s disclosure of two breaches in previous years, Verizon cut its offer by $350 million, or 7 percent of the original price. To reduce such challenges, acquirers should ask several key questions of the target when evaluating a deal, including: Have there been breaches before? Is the intellectual property secure or is it impaired? How mature are the cybersecurity controls and countermeasures, and can they meet current and future needs?

Creating the Right Culture

Years ago it might have made sense for an automaker to look within the auto industry for its next acquisition target. That approach is no longer viable, because emerging technologies have created new markets and spawned many more options for the next generation of drivers. As a result, it becomes ever more important for companies to look outside their immediate industry for expertise and new opportunities, as we saw when Daimler acquired a majority stake in European taxi hailing services MyTaxi in 2014 and Hailo in 2016, and when Toyota made investments in AI/machine learning specialist Preferred Networks.

But integrating the distinct cultures of two companies is easier said than done, especially when an incumbent acquires a tech startup. A common problem is that, more often than not, acquirers forget that they’re not just buying new technologies, but also acquiring the talent and culture that made the startup groundbreaking in the first place. Some tech companies have entrepreneurial and idiosyncratic cultures. They’ve operated as digital companies from the start. They’re also more nimble, more agile, and quicker to experiment and launch new products and services than the big companies. These attributes have made them successful, and it is therefore critical for non-tech acquirers to not only preserve them, but also embrace and learn from them.

Companies should establish an integration plan, setting objectives and milestones for product development, continued innovation, and broader plans to collaborate in the long term. A big part of this puzzle is retaining key talent, but this may include more than just technologists. In some cases, it makes sense to retain the tech target’s management. We saw that with PetSmart’s 2017 acquisition of Chewy.com, in which the online retailer is expected to keep its CEO and operate largely as an independent subsidiary.

Unlike other M&A cases, deals in which an incumbent acquires a tech startup are less about cost cutting and more about leveraging the target’s technical expertise and know-how. It is therefore critical to train employees on the non-tech side with the necessary skills to succeed in the newly merged company. Too often, executives change everything upon acquisition, forcing employees to adopt new ways of working that are antithetical to how they gained success in the first place. This can hurt morale and drive employees to leave the company. Thus, it’s critical for any newly integrated company to include not only high-level executives but also employees firm-wide in its integration process — whether it is creating a steering committee that gives employees a say about changes at the company or scheduling time for them to shadow the tech target’s key employees.

It’s clear that deals in which a non-tech firm buys or partners with a tech startup are a different breed, and the ways they’re negotiated and evaluated come with unique challenges. But the established firms that get it right could well be positioned for high growth.

by Alastair Rimmer

https://www.strategy-business.com/article/Tech-Deals-Bring-New-Challenges-to-M-A?gko=90531&utm_source=itw&utm_medium=20180517&utm_campaign=resp

ANALYTICS SPECIAL .....Ten red flags signaling your analytics program will fail

Ten red flags signaling your analytics program will fail

Struggling to become analytics-driven? One or more of these issues is likely what’s holding your organization back.

These days, it’s the rare CEO who doesn’t know that businesses must become analytics-driven. Many business leaders have, to their credit, been charging ahead with bold investments in analytics resources and artificial intelligence (AI). Many CEOs have dedicated a lot of their own time to implementing analytics programs, appointed chief analytics officers (CAOs) or chief data officers (CDOs), and hired all sorts of data specialists.

However, too many executives have assumed that because they’ve made such big moves, the main challenges to becoming analytics-driven are behind them. But frustrations are beginning to surface; it’s starting to dawn on company executives that they’ve failed to convert their analytics pilots into scalable solutions. (A recent McKinsey survey found that only 8 percent of 1,000 respondents with analytics initiatives engaged in effective scaling practices.) More boards and shareholders are pressing for answers about the scant returns on many early and expensive analytics programs. Overall, McKinsey has observed that only a small fraction of the value that could be unlocked by advanced-analytics approaches has been unlocked—as little as 10 percent in some sectors. And McKinsey’s AI Index reveals that the gapbetween leaders and laggards in successful AI and analytics adoption, within as well as among industry sectors, is growing.

That said, there’s one upside to the growing list of misfires and shortfalls in companies’ big bets on analytics and AI. Collectively, they begin to reveal the failure patterns across organizations of all types, industries, and sizes. We’ve detected what we consider to be the ten red flags that signal an analytics program is in danger of failure. In our experience, business leaders who act on these alerts will dramatically improve their companies’ chances of success in as little as two or three years.

1. The executive team doesn’t have a clear vision for its advanced-analytics programs

In our experience, this often stems from executives lacking a solid understanding of the difference between traditional analytics (that is, business intelligence and reporting) and advanced analytics (powerful predictive and prescriptive tools such as machine learning).

To illustrate, one organization had built a centralized capability in advanced analytics, with heavy investment in data scientists, data engineers, and other key digital roles. The CEO regularly mentioned that the company was using AI techniques, but never with any specificity.

In practice, the company ran a lot of pilot AI programs, but not a single one was adopted by the business at scale. The fundamental reason? Top management didn’t really grasp the concept of advanced analytics. They struggled to define valuable problems for the analytics team to solve, and they failed to invest in building the right skills. As a result, they failed to get traction with their AI pilots. The analytics team they had assembled wasn’t working on the right problems and wasn’t able to use the latest tools and techniques. The company halted the initiative after a year as skepticism grew.

First response: The CEO, CAO, or CDO—or whoever is tasked with leading the company’s analytics initiatives—should set up a series of workshops for the executive team to coach its members in the key tenets of advanced analytics and to undo any lingering misconceptions. These workshops can form the foundation of in-house “academies” that can continually teach key analytics concepts to a broader management audience.

2. No one has determined the value that the initial use cases can deliver in the first year

Too often, the enthusiastic inclination is to apply analytics tools and methods like wallpaper—as something that hopefully will benefit every corner of the organization to which it is applied. But such imprecision leads only to large-scale waste, slower results (if any), and less confidence, from shareholders and employees alike, that analytics initiatives can add value.

That was the story at a large conglomerate. The company identified a handful of use cases and began to put analytics resources against them. But the company did not precisely assess the feasibility or calculate the business value that these use cases could generate, and, lo and behold, the ones it chose produced little value.

First response: Companies in the early stages of scaling analytics use cases must think through, in detail, the top three to five feasible use cases that can create the greatest value quickly—ideally within the first year. This will generate momentum and encourage buy-in for future analytics investments. These decisions should take into account impact, first and foremost. A helpful way to do this is to analyze the entire value chain of the business, from supplier to purchase to after-sales service, to pinpoint the highest-value use cases.

To consider feasibility, think through the following:

·         Is the data needed for the use case accessible and of sufficient quality and time horizon?

·         What specific process steps would need to change for a particular use case?

·         Would the team involved in that process have to change?

·         What could be changed with minimal disruption, and what would require parallel processes until the new analytics approach was proven?

3. There’s no analytics strategy beyond a few use cases

In one example, the senior executives of a large manufacturer were excited about advanced analytics; they had identified several potential cases where they were sure the technology could add value. However, there was no strategy for how to generate value with analytics beyond those specific situations.

Meanwhile, a competitor began using advanced analytics to build a digital platform, partnering with other manufacturers in a broad ecosystem that enabled entirely new product and service categories. By tackling the company’s analytics opportunities in an unstructured way, the CEO achieved some returns but missed a chance to capitalize on this much bigger opportunity. Worse yet, the missed opportunity will now make it much more difficult to energize the company’s workforce to imagine what transformational opportunities lie ahead.

As with any major business initiative, analytics should have its own strategic direction.

First response: There are three crucial questions the CDO or CAO must ask the company’s business leaders:

·         What threats do technologies such as AI and advanced analytics pose for the company?

·         What are the opportunities to use such technologies to improve existing businesses?

·         How can we use data and analytics to create new opportunities?

4. Analytics roles—present and future—are poorly defined

Few executives can describe in detail what analytics talent their organizations have, let alone where that talent is located, how it’s organized, and whether they have the right skills and titles.

In one large financial-services firm, the CEO was an enthusiastic supporter of advanced analytics. He was especially proud that his firm had hired 1,000 data scientists, each at an average loaded cost of $250,000 a year. Later, after it became apparent that the new hires were not delivering what was expected, it was discovered that they were not, by strict definition, data scientists at all. In practice, 100 true data scientists, properly assigned in the right roles in the appropriate organization, would have sufficed. Neither the CEO nor the firm’s human-resources group had a clear understanding of the data-scientist role—nor of other data-centric roles, for that matter.

First response: The right way to approach the talent issue is to think about analytics talent as a tapestry of skill sets and roles (interactive). Naturally, many of these capabilities and roles overlap—some regularly, others depending on the project. Each thread of that tapestry must have its own carefully crafted definition, from detailed job descriptions to organizational interactions. The CDO and chief human resources officer (CHRO) should lead an effort to detail job descriptions for all the analytics roles needed in the years ahead. An immediate next step is to inventory all of those currently with the organization who could meet those job specifications. And then the next step is to fill the remaining roles by hiring externally.

5. The organization lacks analytics translators

If there’s one analytics role that can do the most to start unlocking value, it is the analytics translator. This sometimes overlooked but critical role is best filled by someone on the business side who can help leaders identify high-impact analytics use cases and then “translate” the business needs to data scientists, data engineers, and other tech experts so they can build an actionable analytics solution. Translators are also expected to be actively involved in scaling the solution across the organization and generating buy-in with business users. They possess a unique skill set to help them succeed in their role—a mix of business knowledge, general technical fluency, and project-management excellence.

First response: Hire or train translators right away. Hiring externally might seem like the quickest fix. However, new hires lack the most important quality of a successful translator: deep company knowledge. The right internal candidates have extensive company knowledge and business acumen and also the education to understand mathematical models and to work with data scientists to bring out valuable insights. As this unique combination of skills is hard to find, many companies have created their own translator academies to train these candidates. One global steel company, for example, is training 300 translators in a one-year learning program. At McKinsey, we’ve created our own academy, training 1,000 translators in the past few years.

6. Analytics capabilities are isolated from the business, resulting in an ineffective analytics organization structure

We have observed that organizations with successful analytics initiatives embed analytics capabilities into their core businesses. Those organizations struggling to create value through analytics tend to develop analytics capabilities in isolation, either centralized and far removed from the business or in sporadic pockets of poorly coordinated silos. Neither organizational model is effective. Overcentralization creates bottlenecks and leads to a lack of business buy-in. And decentralization brings with it the risk of different data models that don’t connect.

A definite red flag that the current organizational model is not working is the complaint from a data scientist that his or her work has little or no impact and that the business keeps doing what it has been doing. Executives must keep an ear to the ground for those kinds of complaints.

First response: The C-suite should consider a hybrid organizational model in which agile teams combine talented professionals from both the business side and the analytics side. A hybrid model will retain some centralized capability and decision rights (particularly around data governance and other standards), but the analytics teams are still embedded in the business and accountable for delivering impact.

For many companies, the degree of centralization may change over time. Early in a company’s analytics journey, it might make sense to work more centrally, since it’s easier to build and run a central team and ensure the quality of the team’s outputs. But over time, as the business becomes more proficient, it may be possible for the center to step back to more of a facilitation role, allowing the businesses more autonomy.

7. Costly data-cleansing efforts are started en masse

There’s a tendency for business leaders to think that all available data should be scrubbed clean before analytics initiatives can begin in earnest. Not so.

McKinsey estimates that companies may be squandering as much as 70 percent of their data-cleansing efforts. Not long ago, a large organization spent hundreds of millions of dollars and more than two years on a company-wide data-cleansing and data-lake-development initiative. The objective was to have one data meta-model—essentially one source of truth and a common place for data management. The effort was a waste. The firm did not track the data properly and had little sense of which data might work best for which use cases. And even when it had cleansed the data, there were myriad other issues, such as the inability to fully track the data or understand their context.

First response: Contrary to what might be seen as the CDO’s core remit, he or she must not think or act “data first” when evaluating data-cleansing initiatives. In conjunction with the company’s line-of-business leads and its IT executives, the CDO should orchestrate data cleansing on the data that fuel the most valuable use cases. In parallel, he or she should work to create an enterprise data ontology and master data model as use cases become fully operational.

8. Analytics platforms aren’t built to purpose

Some companies know they need a modern architecture as a foundation for their digital transformations. A common mistake is thinking that legacy IT systems have to be integrated first. Another mistake is building a data lake before figuring out the best ways to fill it and structure it; often, companies design the data lake as one entity, not understanding that it should be partitioned to address different types of use cases.

In many instances, the costs for such investments can be enormous, often millions of dollars, and they may produce meager benefits, in the single-digit millions. We have found that more than half of all data lakes are not fit for purpose. Significant design changes are often needed. In the worst cases, the data-lake initiatives must be abandoned.

That was the case with one large financial-services firm. The company tried to integrate its legacy data warehouses and simplify its legacy IT landscape without a clear business case for the analytics the data would fuel. After two years, the business began to push back as costs escalated, with no signs of value being delivered. After much debate, and after about 80 percent of the investment budget had been spent, the program screeched to a halt.

First response: In practice, a new data platform can exist in parallel with legacy systems. With appropriate input from the chief information officer (CIO), the CDO must ensure that, use case by use case, data ingestion can happen from multiple sources and that data cleansing can be performed and analytics conducted on the platform—all while the legacy IT systems continue to service the organization’s transactional data needs.

9. Nobody knows the quantitative impact that analytics is providing

It is surprising how many companies are spending millions of dollars on advanced analytics and other digital investments but are unable to attribute any bottom-line impact to these investments.

The companies that have learned how to do this typically create a performance-management framework for their analytics initiatives. At a minimum, this calls for carefully developed metrics that track most directly to the initiatives. These might be second-order metrics instead of high-level profitability metrics. For example, analytics applied to an inventory-management system could uncover the drivers of overstock for a quarter. To determine the impact of analytics in this instance, the metric to apply would be the percentage by which overstock was reduced once the problem with the identified driver was corrected.

Precisely aligning metrics in this manner gives companies the ability to alter course if required, moving resources from unsuccessful use cases to others that are delivering value.

First response: The business leads, in conjunction with translators, must be the first responders; it’s their job to identify specific use cases that can deliver value. Then they should commit to measuring the financial impact of those use cases, perhaps every fiscal quarter. Finance may help develop appropriate metrics; the function also acts as the independent arbiter of the performance of the use cases. Beyond that, some leading companies are moving toward automated systems for monitoring use-case performance, including ongoing model validation and upgrades.

10. No one is hyperfocused on identifying potential ethical, social, and regulatory implications of analytics initiatives

It is important to be able to anticipate how digital use cases will acquire and consume data and to understand whether there are any compromises to the regulatory requirements or any ethical issues.

One large industrial manufacturer ran afoul of regulators when it developed an algorithm to predict absenteeism. The company meant well; it sought to understand the correlation between job conditions and absenteeism so it could rethink the work processes that were apt to lead to injuries or illnesses. Unfortunately, the algorithms were able to cluster employees based on their ethnicity, region, and gender, even though such data fields were switched off, and it flagged correlations between race and absenteeism.

Luckily, the company was able to pinpoint and preempt the problem before it affected employee relations and led to a significant regulatory fine. The takeaway: working with data, particularly personnel data, introduces a host of risks from algorithmic bias. Significant supervision, risk management, and mitigation efforts are required to apply the appropriate human judgment to the analytics realm.

First response: As part of a well-run broader risk-management program, the CDO should take the lead, working with the CHRO and the company’s business-ethics experts and legal counsel to set up resiliency testing services that can quickly expose and interpret the secondary effects of the company’s analytics programs. Translators will also be crucial to this effort.

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There is no time to waste. It is imperative that businesses get analytics right. The upside is too significant for it to be discretionary. Many companies, caught up in the hype, have rushed headlong into initiatives that have cost vast amounts of money and time and returned very little.

By identifying and addressing the ten red flags presented here, these companies have a second chance to get on track.

By Oliver Fleming, Tim Fountaine, Nicolaus Henke, and Tamim Saleh

https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/ten-red-flags-signaling-your-analytics-program-will-fail?cid=other-eml-alt-mip-mck-oth-1805&hlkid=87928bc1769a40c5b59cdf78b451bda5&hctky=1627601&hdpid=ec2a5899-4158-4206-a1a4-8e4a5ab1d49e