It used to be a maxim that expanding too fast was the quickest way to kill a successful business. Rapid growth brings risks as well as opportunities. But utilizing augmented intelligence, an already-popular technology which surfaces patterns in data without humans having to look at it, means that organizations can avoid some of the bear traps that lie in that uncharted territory. Businesses using this take opportunities to grow with more confidence. Below, are four ways that augmented intelligence can support businesses growth.

Bringing on new staff

Growing headcount is one of the main ways in which a business expands. Many small businesses operate in so-called “high-trust networks.” They are made up of people who understand the company’s culture and share its goals. Bringing new people on board who are not part of this network, especially if this is done swiftly, carries a number of risks. The new staff may not be as familiar with the business’s way of working. Bringing on board several new staff at once can have the effect of shifting the culture towards a more “low-trust network,” where there is a lot more supervision. That can be difficult for existing staff who may dislike the change, become disaffected and leave, compounding the problem.

Utilizing augmented intelligence can help create what is effectively a high-trust network, but one where the new staff work in a structured environment in which their decisions and actions are visible and where they understand they will be held accountable for their contribution. Within this structure, they can be encouraged to take responsibility for running their own areas of the business.

Avoid decision-making bottlenecks

In expanding companies, managers sometimes create bottlenecks where only certain people can make decisions, in an effort to maintain consistency and control. The company where the CEO makes all the decisions is the company where the CEO doesn’t sleep – and it is also one which is going to run into difficulties as it grows. This situation arises because of fears that people further down the company are going to make mistakes that will impact performance.

Using augmented intelligence to make suggestions to staff, and to record their response, means that humans and the machines can work together to come up with the best solutions. There is less need to worry that people are going to miss important actions or take wrong decisions because of the visibility of what’s going on.

The package delivery service UPS uses augmented intelligence called ORION to shave millions of miles off drivers’ routes – but they also believe in trusting “boots on the ground” and getting drivers to use their own judgement in tandem with the system. In one often-quoted example of human smarts, a fifth grader at a school demonstration questioned a route ORION suggested. The computer planned a list of collections according to location, picking up from a grocery store first. The child pointed out that ice-cream could melt during the rest of the route so it would be in fact better to stop at the grocery store last. Augmented intelligence isn’t a replacement for human problem-solving and decision-making but it can, as its name suggests, augment it.

Managing customer relationships

Another issue that can rear its head as a business grows is the difficulty of managing customer relationships. In a small business, customers naturally build strong relationships with senior staff. But as the business grows and more customers are brought on board, the complexity increases and it becomes harder to get this right. Customer satisfaction can drop.

Tools in use today like Salesforce’s ‘Einstein’, demonstrate how augmented intelligence can be used to manage customer relations in a sustainable and consistent way. Advances in technology create the possibility of monitoring all interactions with customers across many platforms, using this information to make predictions, highlight issues, and to help staff to prioritize their time effectively. This means customers will find that their needs are met at an early stage, and that drives increased satisfaction. Happy customers who come back for more and pass on good messages to others drive business growth, so it is important to manage this to the highest standard.

Keeping track of the financials

Keeping track of the revenue situation is an area that can slide in a period of intense growth. When a business is small, it is relatively straightforward to keep on top of the expected profit and loss and to see if it is headed in the right direction. The chief executive may be able to do the math in his or her head.

But in a growing business, an element of chaos can creep in. There is more revenue coming in, but it is possible that, when the smoke clears and the dust settles, the business might be heading toward the red. This can happen for a number of reasons, such as the inability to invoice fast enough, inaccurate bills, or offering services that are no longer profitable.

For example, a consulting company may agree to a fixed-price engagement requiring four business analysts, but then discover the company does not have the appropriate staff available. The business resource team might then send in staff who do not have the proper skill set. Ultimately, this engagement could end up costing more to deliver than it was sold for. If this sort of mistake happens often, the company’s profitability will soon be affected. Using augmented intelligence allows the business to have a much better idea of what resources will be needed for the work that is in the pipeline, and this creates a valuable window for the human resources team to pull together the best teams to deliver each engagement.

In conclusion, augmented intelligence offers many ways to stay in control of a business as it grows, using powerful tools to manage staff, money and customer relationships that help the company continue to scale.

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Data Science Platforms: Myth v. Reality

The phrase “data science platform” has been bandied about a lot recently — at conferences, in market research, and in tech publications like this one. Forrester named data science platforms a top emerging technology last year, and companies using data science at an enterprise level are being wooed by offerings in a rapidly expanding marketplace of platform providers. But what is a data science platform, really? And is it more than just a buzzword?

First, a definition: Data science platforms are meant to encompass the whole of a data scientist’s work. That means they typically provide tools that help users integrate and explore data from varied sources, build and deploy models, and make the outputs of those models operational. Essentially, this suite of tools is meant to keep data science work transparent, reproducible, and scalable — and make it easy for a data scientist to push dynamic results (like the predicted outcomes of ad campaigns) to the people who make decisions based on those results, replacing or supplementing static (and quickly outdated) reports.  

These platforms are no flash-in-the-pan-product, either. Data science as a profession has blown up — data scientists have had the best job in the United States for two years running according to recruiting site Glassdoor, and data science teams at Fortune 500 companies like Cisco number in the hundreds — and enterprise-grade technology is just beginning to catch up to demand. How do I know? We asked Forrester Consulting to hold a barometer to the industry to find out if — and why — businesses are using platforms*.

The Rise of the Platforms

The last major wave of big data tech investment was focused on enabling data science for organizations: building data lakes, centralizing data, and scaling support to continually integrate data through technologies like Hadoop. But now that companies have access to big data, Forrester has found that data science platform adoption is poised to more than double in the next two years — rising from 29% to 69% by the end of 2018. The reason, the firm concluded, is that more and more companies will soon realize the potential benefits. Among them, survey respondents suggest, are an improved customer experience, more informed business decisions, better business planning, and increased operational cost efficiency and customer retention.

Those aren’t the only benefits to performing data science work around a central software hub. Forrester’s survey also found that tool sprawl, where the volume of tools exceeds an organization’s ability to effectively utilize them, was the number one challenge data-driven businesses face, with an average of 6.7 tools being used to find value in data, from business intelligence tools and relational databases to predictive analytics, streaming analytics, and NoSQL databases. And almost half (46%) of the 208 companies Forrester spoke with lacked an integrated approach to their data science technology stack.

‘Insights leaders’ are the real MVPs

Companies already using data science platforms, on the other hand, are excelling. Forrester identified a group of businesses that regularly exceed profit and growth expectations, which it dubbed “insights leaders.” These leading companies were most likely to be small and agile (53% report having less than 5,000 total employees) and — most notably — 88% of them use a fully functional platform to do data science work. The majority (62%) also have a data science development plan and roadmap in place, as well as top-down support for data science initiatives starting in the C-suite.

Insights leaders currently make up only 22% of the market, and are far ahead of their less data-driven peers when it comes to investing in data science and retaining analytical talent. But nearly every company surveyed — whether insight leader or laggard — reported that data science is an important discipline to develop, and ranks among their most important corporate initiatives.

Clearly, there are a lot of components involved in running a business that does data science well. But as the buzz surrounding platforms becomes steadily louder, it’s my belief that these tools will become a vital ingredient in the recipe for overall business success. Having the ability to iterate on live data models, share code, and push results to other departments doesn’t just affect the reports that land on your CEO’s desk — it informs product development, helps optimize marketing decisions, and much, much more.

*(Full disclosure: We wanted to take stock of the market because we offer a Data Science platform.)

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It’s all well and good to talk about customer experience and managing inventory flow, but what has big data done for me lately?

I’ve rounded up seven of the most interesting — and unique — applications for big data I’ve seen recently and how they may be impacting your life.

Big Data Billboards

Outdoor marketing company Route is using big data to define and justify its pricing model for advertising space on billboards, benches and the sides of busses. Traditionally, outdoor media pricing was priced “per impression” based on an estimate of how many eyes would see the ad in a given day. No more! Now they’re using sophisticated GPS, eye-tracking software, and analysis of traffic patterns to have a much more realistic idea of which advertisements will be seen the most — and therefore be the most effective.

iPhone’s ResearchKit

Apple’s new health app, called ResearchKit, has effectively just turned your phone into a biomedical research device. Researchers can now create studies through which they collect data and input from users phones to compile data for health studies. Your phone might track how many steps you take in a day, or prompt you to answer questions about how you feel after your chemo, or how your Parkinson’s disease is progressing. It’s hoped that making the process easier and more automatic will dramatically increase the number of participants a study can attract as well as the fidelity of the data.

Big Data and Foraging

The website FallingFruit.org combined public information from the U.S. Department of Agriculture, municipal tree inventories, foraging maps and street tree databases to provide an interactive map to tell you where the apple and cherry trees in your neighborhood might be dropping fruit. The website’s stated goal is to remind urbanites that agriculture and natural foods do exist in the city — you might just have to access a website to find it.

Big Data on the Slopes

Ski resorts are even getting into the data game. RFID tags inserted into lift tickets can cut back on fraud and wait times at the lifts, as well as help ski resorts understand traffic patterns, which lifts and runs are most popular at which times of day, and even help track the movements of an individual skier if he were to become lost. They’ve also taken the data to the people, providing websites and apps that will display your day’s stats, from how many runs you slalomed to how many vertical feet you traversed, which you can then share on social media or use to compete with family and friends.

Big Data Weather Forecasting

Applications have long used data from phones to populate traffic maps, but an app called WeatherSignal taps into sensors already built into Android phones to crowdsource real time weather data as well. The phones contain a barometer, hygrometer (humidity), ambient thermometer and lightmeter, all of which can collect data relevant to weather forecasting and be fed into predictive models.

Yelp Hipster Watch

Whether you want to hang with the hipsters or avoid them, Yelp has you covered. With a nifty little search trick they call the Word Map, you can search major cities by words used in reviews — like hipster. The map then plots the locations for the reviews in red. The darker the red, the higher the concentration of that word used in reviews — and when it comes to hipsters, ironic tee shirts and handlebar mustaches.

Even Big Data Bras?

Website True&Co. is using big data to help women find better fitting bras. Statistics show that most women wear the wrong bra size, and so the website has stepped up to try to solve that problem. Customers fill out a fit questionnaire on the site, and based on the responses, an algorithm suggests a selection of bras to choose from. The company’s in-house brand is even developed and designed based on feedback from customers and data the company has collected.

The possibilities of using big data are endless and it might be time to find the big data applications in your business. Have you seen any fascinating or unusual big data projects lately? Let me know about them in the comments below!

This post originally appeared on LinkedIn.

Bernard Marr – the ‘Big Data Guru’ – is one of the world’s most highly respected voices anywhere when it comes to data in business. He is a highly acclaimed keynote speaker and advises companies and government organizations on how to use big data, analytics and metrics to improve strategic decision-making and boost company performance. His new book is: Big Data: Using Smart Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance.

Photo credit: Melissa Maples / Foter / CC BY-NC

Paul Forrest has some twenty-five years experience of helping businesses to solve complex business problems, deliver their transformation goals and to achieve tangible strategic outcomes. Hands on and deep thinking, he has worked with many FTSE 100 clients, Global 500 businesses and major Government entities around the world including Ford Motor Company, VAG Group, BAE Systems, GSK, AkzoNobel, RBS, HBOS, Diageo, Bacardi, Wal-Mart, British Airways, Virgin Galactic, Etisalat, British Telecom, Vodafone and Allen & Overy. Paul also sits on the board of a number of agile, disruptive and challenger businesses whilst supporting value creation programmes for Private Equity backed businesses. He joined the board of MBN Solutions to help establish the platform for growth and focuses on the development of the senior team, management practices and MBN’s core focus around Insight and Analytics.

Not realising the potential benefits of ‘Big Data’? Here, I present a little food for thought at the start of the year doused with a little common sense. Sometimes a gentle nudge or reminder of what’s missing helps to focus the mind on what skills a business should be searching for. With this in mind, I introduce my take on the need for more ‘Bridging’ skills in organisations seeking to benefit from ‘Big Data’.

So, it’s 2015- what do we know about how Data-Driven Companies are performing?

You can’t help but to have noticed that Big Data is growing in popularity for column inches in the business press. Everywhere from the mainstream broadsheets right through to regular articles in the Harvard Business Review one can learn of the latest perspectives on Big Data and the likely relevance to your business. So why is it that we arrive at the start of 2015 with many businesses struggling to make sense of the opportunity and how to go about making the most of it?

Ok, so lets start with my regular health warning here… These are my opinions and observations. They are based on the work I perform at board level with many leading, large listed businesses seeking to get the most from their insight and data enabled initiatives. Whilst I’m not the greatest fan of the ‘Big Data’ expression, I use it here in the same way people use ‘bucket terms’ in business. It serves a purpose and is not intended to differentiate the myriad initiatives that exist in this space.

Context

According to Gartner’s recent Big Data Industry Insights report, it’s clear that many organisations are increasing their investments in Big Data. What is clearer still is that a large proportion continues to struggle to gain significant business value from those investments.

Add into the mix other sentiment and survey results such as a recent CSC survey and you discover that five of every nine Big Data projects never reach completion and many others fall short of their stated objectives. So what can these surveys offer in terms of clues?

My own analysis here suggests no real surprises. Often, board level sponsors, business managers, data and IT groups are not aligned on the business problem they need to solve. Also, employees frequently lack the skills required to frame insight questions or analyse data.

Many more simply don’t take a strategic approach to their initiatives and fail to follow what we have come to regard as cornerstone rules (more on these later). So, is there a ‘Silver Bullet’ available to help solve these issues?

Silver Bullet?

Many of us have come to understand this term as a general metaphor, where ‘Silver Bullet’ refers to any straightforward solution perceived to have extreme effectiveness. The phrase typically appears with an expectation that some new development or practice will easily cure a major prevailing problem.

Without wishing to be too controversial, my analysis of the missing link could yield ‘extreme effectiveness‘ but not as a result of a new development.

So what are we discussing here? In its simplest terms, I’m referring to a role best described as ‘The Bridge’. ‘The Bridge’ is a reference to a skillset more than a specific role. It may even be a cultural characteristic but what is clear, is that for many organisations failing to realise the benefits they seek from data, the root cause is the disconnect between key stakeholders including the board, the IT department, those at the sharp end of the business and those responsible for processing, storing and analysing the data available to the enterprise.

Now… to be clear, this is not necessarily an independent role I see as being required in isolation of other activities – think of it more as a matrix management activity or a core skill requirement for middle and senior management. In essence, if you could equip your business analysts, data scientists, insight staff and IT team with these skills in a coterminous fashion; I think you’d be close to the desired state. But this requires education; awareness and a heap of soft skills still often overlooked in corporate staff and management development programmes.

Buy or Build?

The perennial question of whether or not you develop your own or simply hire someone with the requisite ‘Bridge’ skills and characteristics is a challenging one for many businesses. These are rare people. Managers at the front end of the business with a deep enough appreciation for how data can answer previously unanswered questions, IT practitioners with deep, relevant and up to date domain expertise, data practitioners with the ability to translate their knowledge into easy to follow guidance for business are not likely to be readily available.

‘Buying’ them in is equally challenging. As already noted, these are not necessarily people with a role literally and exclusively relating to the tenets of this paper. Instead, they are likely to be traits you have to specifically look for whilst recruiting other staff. Perhaps the closest cousin is the Business Analyst. I prefer to consider these skills and traits which all senior managers should be able to master to ensure that the expertise is rooted in the business and available ‘on tap’. This means that unless you’re in a hiring cycle, you may need to consider building your own ‘Bridge’.

 Building The Bridge

So if The Bridge is the missing link to get you from the mass of data, analysts and technology to a real, positive business outcome, what should you be looking for?

Awareness and Education

Tremendous appreciation for the strategy, people, systems and operational characteristics of the business provides an obvious start for the necessary awareness. In terms of education, the starting point must be greater awareness, in terms of what the art of the possible is… what can be achieved and what the thought process and practical approach to framing issues and improvement opportunities must be at the heart of this. Some high achieving organisations I work with consider this is something that should be endemic across the whole business and at all levels. The best way to achieve this is likely to be an item of debate for some years to come but in essence, the key methods include:

• Reciprocal secondments
• In-house and external Training and seminars
• Enhanced/structured continuous professional development
• Online communities
• Individual reading
• Business Safaris – Reading groups
• Programmed action research
• Individual action research
• Membership of professional bodies

Whilst not intended to be an exhaustive list, there should be few surprises here. The issue for many is that most soft skills training tends to be focused on developing deeper skills utilised for the delivery of ‘business as usual’ rather than developing a whole new awareness of a data driven business. Perhaps its time to think outside of the box?

Soft skills to deliver ‘The Bridge’ 

There are a number of specific areas of focus including those that are more readily incorporated into soft skills training. These may, to some extent, seem like common sense (and perhaps they are), but start by looking at your key managers and match these soft skills on the basis of competency on one axis and capability on the other… How many appear in the top right quadrant?… So, whether you seek to build the skills into your own team or recruit key team members to be your ‘Bridge’, the starting point, as a minimum, are the following.

Ability to Communicate

The nature of The Bridge means that they’ll be spending much of their time interacting with people from a variety of backgrounds across the business. This ranges from users, customers or clients, to management and teams of scientists, analysts and IT developers. The ultimate success of an initiative depends on the details being clearly communicated and understood between all parties, especially the project requirements, any requested changes and results from the testing.

Technical Skills

For your ‘Bridge’ to be able to identify solutions for the business, they need to know and understand the data available to the business, its current use, storage, ownership and currency. Remember, you’re not expecting people here to be able to craft their own solutions and develop deep data taxonomies etc. Bringing ‘bridge skills’ into play necessitates understanding new outcomes that can be achieved with the data worked in a different way – answering previously unanswered questions. This in turn means having a good understanding of the benefits the latest analytical approaches and the insight, analytics and scientific skills available to or within the business offers. Ultimately, you will need a data scientist, analyst or insight specialist to ‘make the magic happen’.

The ability to test data solutions and hypothesis and design business what if scenarios is an important technical skill, and they’ll only gain respect and build confidence in the Data Science teams and business end-users if they can demonstrate that they can speak with authority in the dual languages of business and data, whilst being technically strong in the appreciation of data tools.

Analytical Skills

To be able to properly interpret and translate the business’ needs into data and operational requirements, every ‘Bridge’ needs very strong analytical skills. A significant amount of their job will be analysing data, documents and scenarios, reviewing user journeys and operational workflow to determine the right courses of action to take.

Problem Solving Skills

Building on their analytical skills, your ‘Bridge’ will also need to be able to look at the data and use out of the box thinking to help craft solutions for their business users. Again, clearly, they are not the main practitioners responsible for crafting a solution; this is the domain of the Data Scientist or insight analyst etc. No, our ‘Bridge’ here is responsible for facilitating the efficient and effective interplay between the key stakeholders from the business and the data practitioners.

Decision-Making Skills

It is key in any business that the board or key decision makers do not abdicate management responsibility. However, your ‘Bridge’ should be available to help make important decisions in the data solution building process. The ‘Bridge’ acts as a consultant to managers and the advisor for the data and IT teams together, so they need to be ready to be asked any number of questions. Your ‘Bridge’ needs to know how to assess a situation, take in relevant information from your stakeholders and work collaboratively to plan out a course of action.

Influencing and Negotiation skills

The ‘Bridge’ is the liaison between the users, data scientists, analysts, developers, management and the clients. This means a careful balancing act of differing interests whilst trying to achieve the best outcome for the business. To do so requires an outstanding ability to persuade and negotiate.

Agility

Corporate agility is a trait for an organisation wishing to get the best out of a ‘Bridge’. However, individual agility is a mandatory requirement. Agile and flexible, and have no trouble taking on the unique challenges of every new business project with a rich data driven theme and mastering both the requirements and the personalities in the collaborating teams.

Management Competencies and Change Management Traits

The most successful ‘Bridge’ is likely to have a skill set that allows them to demonstrate their abilities across the board, while being experts in managing business processes, developing a project, focusing on outcomes, strategic planning, change management and being able to communicate effectively to their business partners. They need a strong fundamental set of data knowledge, systems and tools, be able to grasp engineering concepts and be aware of complex modelling techniques together with the ability to write effectively on such subjects in plain and simple language. If they master all of these skills, they’ll be a phenomenal ‘Bridge’, that any business would be lucky to have on board.

Conclusion 

For many businesses, Big Data initiatives are not failing per se, it is simply that the organisations facing failure have yet to find an implementation model that allows them to exploit the initiatives to deliver expected outcomes. This is a matter of Business Maturity and assembling the right team (see the rules below). In many cases where failure prevails, it is the absence of the right skills that has caused the failure. To help overcome these hurdles and maximise business value from data, organisations should seek out those in their ranks with the skills to be the ‘Bridge” and then consider the following steps to significantly shorten the time-to-value and contribute to business success using big data initiatives.

My advice? Follow these simple rules:

• Maintain one instance of each data set not multiple copies (thus avoiding version control issues and problems with validity or currency of data)

• Find ways to focus on faster deployment of data – the faster the more valuable the outcome – particularly when it comes to predicative analysis

• Consider diverse data sources including social and collaborative data sourcing from peers, competitors and data exchanges.

• Data has value – not always immediately, so keep what you can and focus on exponential growth of your data storage needs

• Use your ‘Bridge’ to work with the business to identify and plan to solve real pain points. Identifying the unmet need is key and interpretation of the pain into tenets of a solution is where your ‘Bridge’ should really shine. They can then help specify solutions for the data scientists and analysts to build with the IT team.

• Ultimately, it’s about bringing the right people together at the right time and facilitating their buy in and commitment to business solutions to complex business problems. Get it right and maximise the prospects of your ‘Big Data’ initiatives.

Want help to find people who fit the bill? DataIQ’s Big Data Recruitment Partner,MBN Solutions can help. Alternatively, if you are wrestling with the lack of a ‘Bridge’ for a specific delivery initiative or project, contact Advanced Capability Solutions Big Data team via the link.

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