July Insights On Big Data & Business Transformation

It’s been a busy month in Big Data, and we’ve been deep in learning about what factors are driving the industry. We’ve translated our experience, research and insight into five key lessons.

Here’s what we’ve learnt this month.

 

#1 – PLAN & TEST FIRST, INTEGRATE LATER

“Big data has proven to be a valuable business asset, but using it to gain competitive advantage requires the right combination of strategy, technology and execution.” – Mahmood Majeed: Managing Partner at ZS Associates.

In all the excitement of implementing a Big Data strategy, It’s easy to get caught up in the hype of new technologies promising to do magical things with your data, and make hasty decisions that once integrated, can be disappointing.

To avoid this, team leaders of Big Data projects must ensure that expectations and output are aligned.

“Start by developing a strategy across the entire enterprise that includes a clear understanding of what you hope to accomplish and how success will be measured.” – Harvard Business Review.

 

#2 – SECURITY IS NOT AN AFTERTHOUGHT, IT’S A NECESSITY

“Big Data and analytics is showing promise with improving cyber security. 90% of respondents from MeriTalk’s new U.S. government survey said they’ve seen a decline in security breaches.” – SentinelOne.

With the volume of worldwide data reaching unprecedented levels, new cyber security threats are emerging daily. To combat this, an article in CSO discusses the benefits of using historical data to identify potential cyber attacks while also predicting future events.

“Using this historical data, you can create statistical baselines to identify what is ‘normal’. You will then be able to determine when the data deviates from the norm.This historical data can also create new possibilities for predictive models, statistical models, and machine learning.”

 

#3 – IT’S ABOUT QUALITY, NOT VOLUME

“Because Big Data presents new features, its data quality also faces many challenges.” – Li Cai & Yangyong Zhu: Fudan University.

As data evolves, new challenges emerge which is why it’s important for businesses to develop data quality standards. With the rise of insight-driven business models, the quality of the data used is key to making the right decisions. Leaders of Big Data projects must ensure that the data is accurate for the intended use.

“Data quality depends not only on its own features, but also on the business environment using the data, including business processes and business users.” – Data Science Journal.

 

#4 – BIG DATA + AGILE = SUCCESS

“The longer you take to find the data, the less valuable it becomes.” – Wired.

With recent advancements in technology, more emphasis is being placed on data agility and the importance of data-driven insights in real-time. “How fast can you extract value from your mountains of data and how quickly can you translate that information into action?” – tdwi.org.

Ian Abramson, former Director at EPAM Systems, describes the alignment of Big Data and Agile as “the infrastructure and framework to be successful.” He then goes on to talk about how this alignment enables focus and a clear picture of how to get from A to B. “ What is the question, what is the success factor, how will we get there, and who will be involved?”

At the 2016 International Conference on Ambient Systems, Networks and Technologies (ANT), experts in the field of Big Data were asked, “ What do you think is most important in the management of Big Data projects?” Interestingly, respondents rated three out of the Four Values of The Agile Manifesto as the highest in their answers, which were: cooperation with customers, people & communication, and working with software over comprehensive documentation.

 

#5 – SKILL-UP OR MISS OUT

“Is the world going to become a place in which automation is everywhere yet employment is scarce?” – Michael Grothaus: The Hanbury Literary Agency.

According to KPMG’s 2016 CIO Survey, data analytics is the most in-demand technology skill for the second year running, but nearly 40% of IT leaders say they suffer from shortfalls in skills in this critical area. “Big Data training is beneficial in meeting the demands of Data Management, faster decision making, better understanding of customers and tapping into the right demographic.” – Vikrant Singh: Senior Manager at Xebia Group International.

In a recent article , Sophia Bernazzani, Marketing Manager at HubSpot says, “The fact remains that some jobs will be replaced by machines – it’s the essence of any industrial or technological revolution. The good news is; some jobs won’t be strictly replaced – they just might be adjusted to account for new technologies.”

So how do we skill-up and adopt these new technologies into our businesses? “By training those already with the company, businesses get to keep valuable team members that already have experience with the enterprise while giving them some much-needed skills.” – Rick Delgado: Enterprise Tech Commentator & Writer.

“Training employees can never be a liability. It is in fact an asset, an investment to the company.” – Big Data Trunk.

 

For more resources, please see below:

 

Data Integration

How To Integrate Data And Analytics Into Every Part of Your Organisation

Charting An Effective Big Data Strategy

Big Data: The Management Revolution

 

Data Quality

Beyond Volume, Variety & Velocity Is The Issue of Big Data Veracity

The Challenges of Data Quality Assessment In The Big Data Era

Big & Fast Data: The Rise of Insight-Driven Businesses

 

Cyber Security

How Big Data Is Improving Cyber Security

Big Data Security Analytics: A Weapon Against Cyber Security Attacks

 

Data Skills

The Importance of Employee Training

Businesses And The Big Data Skills Shortage

Big Data Jobs Are Out There: Are you Ready?

The Importance of Big Data Training to Your Data Analysis Growth

10 Jobs Artificial Intelligence Will Replace (and 10 That Are Safe)

 

Data Agility

Big Data and Agile: The Perfect Marriage

Agile Project Management And Its Use in Big Data Management

Looking For A Career In Big Data Engineering? We’re Hiring At Contexti

Location: Sydney, Australia

Big Data is changing the world, and adapting to an era of more data-driven decision making requires developing talent, processes and organisational muscle. With the rapid pace of innovation and growing demand for Big Data, the data engineering space has more opportunities than ever before.

Contexti is a specialist Big Data Analytics solutions company serving the Australian market. With expertise in Commercial Data Strategies, Big Data Platforms, Data Science & Insights, we enable our customers to drive growth, innovate and compete.

As a trusted Big Data specialist solutions partner for Amazon Web Services (AWS), Cloudera, Mesosphere and Talend, we provide Advisory, Consulting, Training and Managed Services for tier one clients.

Check out the work we did with Seven West Media on audience engagement for the Rio Olympic Games:

Seven West Media Taps Cloudera and Contexti For Big Data Solution For Rio Olympics

Due to strong organic growth, we are now looking for additional team members.

 

The Opportunity

We’ve got a number of entry level and mid-level opportunities for engineers keen on developing their careers in Big Data Analytics.

Our consultants get to develop their technical and client service skills working with leading Cloud and Big Data technologies.

We provide both internal and external training, and you will get to work alongside some of the best in the industry.

 

About You

The person:

  • Looking for purpose in your work and the company you work for
  • You’re ready to learn, grow and contribute
  • You love to help customers and team members

You have one or more of the following:

  • Experience in Data Engineering (SQL)
  • Experience with Hadoop, Spark, EMR, Oozie
  • Experience with infrastructure and automation
  • Experience with Amazon Web Services (AWS)
  • Experience working with DevOps tools, Chef, Ansible, Puppet

 

How to Apply

  • You must hold the right to work in Australia to be considered for this role.
  • Please send your resume and short cover note to jobs@contexti.com

Note to Recruiters – We will be filling these roles directly.

 

 

 

Data & Analytics Australian Recruitment Market Insights by FutureYou

With the launch of their data and analytics practice lead by Caroline McColl, FutureYou Executive Recruitment have produced an insightful report on the data and analytics recruitment market in Australia. This 3 page reports gives a great snapshot on:

  • Market Moves – who are the new Chief Data Officers, Heads of Analytics and Heads of Insights?
  • In Demand Skills – do you have what the market is looking for?
  • Industry Pain Points – key challenges in getting value from data – are you having the right conversations?
  • Candidate Spotlight – what’s the going salary rate, for which skills and industry experience?
  • Skills Testing – why would you get your candidates skills tested?
  • Top 5 Drivers for Career Advancement – What’s the ideal combination of skills across business, technology and data science that will advance your career in big data analytics?

FutureYou have kindly permitted Contexti to share this report with those interested. If you’re hiring or are seeking to get hired this report will provide some food for thought.

5 Tips On How To Land A Big Data Job In Australia

In today’s Australian job market if you’ve got some Big Data experience, you’re mostly likely getting approached by recruiters and are probably spoilt for choice. If that’s the case, you don’t need to read on. This article is for the rest of you, who have heard about this ‘Big Data’ thing and are wondering how to get your foot in the door.

Given Contexti’s focus on the Big Data Analytics market in Australia, we’re fortunate to be aware of and in many cases involved in a broad range of Big Data Analytics related conversations, deals, projects, partnerships, hires, fires and events across Australia. The single biggest challenge we constantly hear about is the shortage of qualified and experienced ‘Big Data’ people.

While we don’t advise our customers to drop their standards in the quality of their hires, we do strongly warn against holding on to the belief that there is a magical unicorn big data guru out there. Instead we suggest organisations hire professionals with the right fundamentals (e.g. fit for culture & values, coachable, possess skills in certain technologies or analytics methods, etc) and implement a plan to develop them into capable Big Data Analytics practitioners.

Similarly we’ve found ourselves having conversations with a broad range of professionals, some who are just starting our their careers and are thinking about graduate roles while others with decades of experience who now want to transition into a career in the growing Big Data Analytics space.

Like everything else in business and life there are no silver bullets, but if you approach this in a strategic and tactical manner, you will massively improve the odds in your favour.

So here are five tips to help you land a big data job in Australia:

#1 Define your target role

While ‘data scientist’ sounds like an exciting role, it may not be the right entry point for you. You want to get into a role where you will learn and where you will also quickly add value by bringing something to the table. To do this, think about your ‘home ground advantage’, what skills, experience or connections do you already have and map it to the closest Big Data role in the an industry most suitable to you.

Some real-world examples we are aware of:

  • Our own Damion Reeves at Contexti transitioned from being an experienced Database Administrator (DBA) with years of experience in infrastructure, Oracle and SQL to a Big Data Platform Engineer. While Hadoop and Spark were technologies he needed to learn, his underlying experience with Linux and UNIX, capabilities in shell scripting and knowledge of enterprise support and service protocols were immediate value-adds to Contexti and to our customers.
  • Our client Sharmaine Salis Head of Data Architecture at Seven West Media transitioned from a traditional Business Intelligence / Data Warehouse solutions role into a Big Data / Cloud Architect role, leading one of the most successful big data projects in Australia which underpinned Seven’s Rio Olympics games coverage.
  • One of our Hadoop & Spark training students, MingJian Tang currently a Cyber Security Data Scientist at the Commonwealth Bank of Australia (CBA), transitioned into this role from a statistics and data mining background.
  • Broader in the field we’ve seen someone with solid Telco background move into a Big Data Strategy role for one of the Telcos trying to monetise their data assets.

So the take-aways are:

  • There are many potential Big Data Analytics roles (Commercial Strategist, Platform Architect, Data Architect, Platform Engineer, Data Engineer, Analyst, Data Scientist, Project Manager, Quality Assurance, Sales, Business Development, Customer Success etc).
  • No one-person will be qualified to do all the available roles in Big Data.
  • Find your home ground advantage and target a role that gets you excited and one where you can add value quickly.

#2 Skill up

You will massively improve your chances in landing a role if you’ve invested in skilling yourself up. The one obvious benefit is the theoretical and in many cases the practical knowledge you will gain by attending formal training. The other not so obvious benefit is the network of relationships you will create with the instructor and other class participants. Depending on the role, your budget, time availability etc there are many courses to take advantage of. Here are some of the short to long training and certification programs we are aware of:

#3 Network

An important factor in landing a new role is ‘who you know and who knows you’. Networking enables you to to build relationships, get known, learn something new and contribute. There are many meetup groups and networking events. Here are some of the ones we attend:

#4 Be found

There are many ways to get your name out there and to be found. Speaking at events and meet ups, writing guest blogs posts, publishing your work in online forums (GitHub, Slideshare etc), getting active on Twitter and Quora. The simplest and most obvious one however is to put effort in your LinkedIn profile. After you consider your target role and your home ground advantages (existing skills, industry experience etc) as well as your training and up-skilling strategy, you should update your LinkedIn profile.

Your profile should be authentic. This means stating correctly what you have done, skills you possess and how much experience you actually have. Further an authentic profile should include objectives, aspirations and current activities you are undertaking to improve yourself giving the potential recruiter an idea of not only where you’ve been but a view of where you are headed.

A recent example was when I was doing a search on LinkedIn for anyone who had included “Data Science” in their profile. I came across a professional who had recently completed a data science course in addition to having a math and statistics major and hands-on actuarial work experience. His LinkedIn headline said ‘Aspiring Data Scientist’. The word ‘aspiring’ gave me an indication of where he was headed and what he was looking for yet it was authentic as he wasn’t claiming to be an experienced data scientist.

This approach can be applied to your LinkedIn headline and your summary where you can include your ‘elevator pitch’ of who you are, where you’ve been, what your great at and where you are heading.

#5 Look for early signals

To narrow down your targeting efforts and improve your odds, look for early signals that might lead you to a future job opportunity. Typically this will be keeping your eyes open on LinkedIn, subscribing to relevant industry news and blogs, reading mainstream business and technology news and being an active networker. Early signals you should keep your eyes on include: companies announcing changes in strategy, appointment of new leaders, new partnerships or vendors winning contracts.

For example in the last six months in Australia there have been a number of executive movements in the Chief Data Officer and Chief Digital Officer roles, this kind of appointment usually indicates a company is reprioritising ‘data’ as a strategic priority and is usually followed by a restructure and a recruitment drive. There have also been a number of public announcement of data deals and data partnerships as well vendors announcing contracts with new customers or publishing case studies of success stories with existing customers.

All of these are early signals that will give you hints on people, companies, technologies and deals to follow and target in order to land your next big data job.

 

By: Sidney Minassian – Founder & CEO, Contexti – Big Data Analytics

 

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7 Ways to Increase Your Value as a Data Scientist

As a Data Scientist, you are currently in high demand and in a hot market. That is not about to change any time soon.

So on the one hand, you can ignore this article.

Yet on the other hand, given the 40+ big data projects we’ve delivered for Contexti clients, we’ve had a lot of interactions with Data Scientists inside companies of varying sizes and industries and my observation is that Data Scientists are leaving value on the table for themselves and are therefore limiting their career and leadership trajectories.

There are probably a number of reasons for this, but primarily it’s because Data Scientists are allowing themselves to be pigeonholed into being ‘just the data person’.

You will have greater value as a Data Scientist when:

  • you have established credibility BEYOND being just the data person
  • you get a seat at the strategic table to discuss the business and customer context; and
  • your efforts result in measurable impact on the organisation

So here are 7 ways you can increase your value as Data Scientist:

#1 Know the business

When you are on the ‘same page’ as the business you will engender a deeper level of conversation, you will ask better questions, you will push back on the right issues and overall you will command the respect of your colleagues beyond your analytics brilliance. You should know and be able to quickly articulate key business and profit details such as:

  • what industry you are in;
  • what are the top performing product / service lines;
  • what are your best channels to market;
  • who are your primary customers;
  • who are your most strategic partners (and why? what’s in it for them);
  • who are your biggest competitors and what is their strategic or competitive advantage;
  • who are your likely unexpected competitors and
  • who is going to disrupt you or your industry externally ◦ etc

 •

#2 Get to know your Customer’s Customer

Don’t settle with just understanding the ‘Marketing’, ‘Risk’, ‘Operations’ departments as your customers. While they may be your direct customers, you should also care to learn about your customer’s customer. Who are they serving? Request that you join your customer when they meet with their customers, this will give you another level of context, perspective and depth in understanding the ‘end customer’. By getting to know your customer’s customer, you will think differently about the problem you are solving and you will have a different conversation and create a higher level of rapport with your direct customer.

#3 Beyond the WHAT and the HOW…. Ask WHY

It’s easy to jump into problem solving mode. The question is are you solving the right problems? Often you’ll have clarity on ‘What’ you need to do and given your skills you’ll know the ‘How’. To increase your relevance and value, make sure you are also clear on the ‘WHY’. By understanding the ‘Why’ you will think creatively about the problem and solution – if you understand the ‘why’ you may recognise you’ve been tasked with the incorrect ‘what’. Even the conversation of the ‘why’ will help build trust between you and the people you are collaborating with. •

#4 Step away from the data

To get context and perspective, step away from the data, models and charts and put yourself in position to observe what you are meant to be measuring or solving in its physical form. Things beyond the numbers will jump out at you that can dictate the success or failure of your solutions. Certain industry nuances, the political landscape of the organisation, the organisation’s readiness to adopt change, culture and values, the user experience and the customer journey will all give you greater levels of insight beyond the numbers.

#5 Seek the ACTION

The best insights, not executed will create zero value. So in addition to understanding the ‘why’, seek to understand the ‘actions’ that will be taken given your insights. Often this will be outside of your domain or direct sphere of influence and that is exactly the point. To move beyond being ‘just the data person’ you should seek clarity (accountability) from your colleagues on what will be done with your insights, in what time frame and how you and your colleagues will be informed about the impact.

#6 Build bridges with people

The right team composition is critical to ensure success with data projects. In addition to Data Scientists you need customer advocates, subject matter experts, platform architects, platform engineers, data engineers, platform administrators, marketing/operations/risk/legal experts etc. So as a Data Scientist, build relationships with these colleagues, they are all important contributors to delivering success. You will learn from them, you will teach them and most importantly you will have established a bridge, which will raise your value. •

#7 Over-Communicate

We often hear Data Scientists need to be ‘story tellers’, often this is only interpreted as ‘story telling with the numbers’. I suggest that you should not to wait for just the ‘story with the numbers’ part of the project before you find your voice. It’s important to bring people on the journey and you can do this by communicating (over communicating). Share with them your understanding of the ‘business’, your knowledge of the ‘customer’s customer’, the ‘why’ of what you are working on, the insights you gained by ‘stepping away from the numbers’ and how you expect the your insights to be turned into ‘actions’ to deliver value. Share with people your experimentations, your success, failures and learnings. You will learn from their feedback and corrections, you will build respect with your openness and willingness to share and teach and you will establish your voice in your organisation.