Analytics

Data & Analytics

Generating better insights from your business systems

We work with organizations to build a holistic data strategy to effectively communicate business insights

what we do
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We drive smarter executive decision-making by cutting through data-driven opinions, and aligning corporate goals with singularly defined metrics for success. Unless this is accompanied by the simultaneous creation of a strong foundation for taking intelligent business actions, they are unlikely to reap a good return on that investment. Without the right analytic approach, no amount of investment will translate to insights.

BI tools like Tableau, Qlik & PowerBI are only part of the solution. We focus on the inflow and output of data, whether that's the right data to be processing, and eventually, work on recognizing patterns within that information. In that way, our Data & Analytics team focuses on creating decision-support frameworks that enable leaders to act on the information we're presenting.

Industry Knowledge

Data is being collected by disparate systems, operates independently from a reporting standpoint, uses different database schema, and ultimately – is far from integrated. As the amount of data and necessary fields grow – they begin to overlap less and compound the existing integration problems

Enabling Decision Making at the CxO Level

The number one problem that businesses encounter when making revenue-facing decisions for the company is data deluge. In today's business landscape, important decisions need to have backing from insight data via intuitive tools. For any given problem, there are 4 different solutions, each completely validated by their own 'data stories'. For every CxO, the challenge in being able to separate the narrative from the cold-hard facts that truly enable sound decision making.

Every growing business inevitably deals with the pains of exponentially increasing data points. As this growth occurs, it becomes harder to keep track of data spread across different verticals within the business. One of the biggest shifts businesses have to make is to examine their analytics framework. Icreon specializes in reducing the number of 'stories' your data is telling you, while also enabling you to feel confident that you're acting on the right information.

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thoughts

Some of the considerations we think about in our approach to Data, Business Intelligence & Advanced Analytics

Thoughts and Considerations

Discoverable

Revealing insights that are surfaced by deeply exploring opaque datasets

Self-Service

Empowering users and departments to be their own data ambassadors

Composable

Developing insights by combining disparate sources of information

Predictive

Leveraging past data to create educated forecasts about the future

Decision-Driving

Building confidence in decisions by backing it with past performance

Visual

Digesting abstract trends by identifying effective visualizations

Google Analytics
Google Data Studio
PowerBI
Python
R
Tableau
Tensor Flow
Thunderhead

Just a few of the tools, frameworks & platforms in our arsenal

solution in action
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deeper

The Value in Search Based Data Discovery

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Data Visualization Trends that Matter in 2018

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How Business Modernization Helps to Enable Open Data

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Machine Learning will only go as far as the Data it Learns From

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The Value in Search Based Data Discovery

Did you know that in 2020 the world will produce 50 times the amount of data that it did in 2011? With that in mind, it’s important to understand how today’s organizations need to integrate and derive insight from a growing number of multi-structured data sources in order to drive innovation. In this article we’ll show you how integrating a data discovery tool can help you create a smarter, more efficient data discovery process within your current workflow.

First, let’s answer the most fundamental of questions: what is a search based data discovery tool?

In short a search based discovery tool allows its users to cultivate and improve views and analyses of structured and unstructured data using search terms. Think of it like visualization-based data discovery tools, as they work in many of the same ways, only search based data discovery tools utilize text search in order to garner the needed information. Two of the current leaders in search based data discovery are Tableau and QlikTech.

Tableau is one of the leading data discovery tools, and a lot of that has to do with its accessibility. The product is both easy to use and to learn. It also links analysts to a myriad of data sources, both specific sources as well as in memory technology. You can build appealing and valuable that can be shared across the company via being published on the server.

Qlick is Tableau’s direct competitor and they also have a product that makse analytics truly intuitive for anyone in need of creating dashboards that help aggregate and visualize disparate data points.

Regardless of which discovery platform you choose to work with, both allow for the connections in your data to become visible, providing an opportunity to see patterns from every angle. They do this by concentrating on usability, manageability, transportability, enabled data discover, and pleasing visual aesthetics.

In this next section we’ll focus on four different ways of implementing these tools which can help your business realize its innovation goals.

Breaks Down Data Silos

A data silo is when only one department dominates a repository of fixed data—just like in farming when a grain silo is isolated. However, because silo data can only aid one specific group of analysts, discovery data tools give employees from all departments access to information across the business. By working with a discovery data tool you can allow cross department collaboration like: engineers the power to see sales data in order to understand what designs have been flourishing in the market, and what improvements need to be made.

Promotes an Analytical Culture

Everyone in your company should not only have access to data, but should also be able to analyze it in order to cultivate a deeper level of decision making. Getting data discovery tools not only creates an atmosphere of collaboration across departments, but moreover, creates an analytic culture, where all employees are looking to the data for answers. The value of an analytic culture is that it inspires users to understand the importance of data from other departments, and vice versa.

Helps Build Trust

Recent Aberdeen research labeled “trust as one of the three pillars of a successful big data strategy.” And using a data discovery tool, which enables a business to connect with a diverse volume of data, enables the user to trust the data they’re analyzing, as well as make a deeper analytic decision—rather than one made on instinct alone. Furthermore, when users make an informed decision they are 71% more likely to be satisfied with their ability to access higher volumes of data, which indicates that they also feel that their data was not prescribed to them. Furthermore, they won’t have to face calling for a solution that needs data which is not readily available.

Predicts Risk Implications

Aberdeen also states that nearly two thirds of organizations employing a data discovery tool are in finance, but that organizations across the board—from corporate management, sales, marketing and customer service— could benefit from implementing one. This is because a data discovery tool creates pipeline of information that can be assessed for future risk, creating a compelling vision of the risk implication of different situations.

In the past you had to implement teams of analysts and data scientists together in order to interpret your data, but with a data discovery tool, you can simply talk to the software, and get answers. When the both the CEO and the sales manager feel they have the same skills to engage with data via a discovery tool, everyone in the company is empowered to work with the data, thus moving every business decision forward.

Data Visualization Trends that Matter in 2018

When it comes to data visualization, your audience (colleague, customer, client, reader - whoever!) is key. And in the current year and beyond, it's what the viewer wants that will shape data viz trends. That is, while we might have all the data, the way we need to present that data to satisfy consumer demands is constantly changing.
So, what's going to change? And is your business or brand ready for these expectations? Find out - here are our top 5 picks for trends that will change data visualisation in 2018...
1. Interactive Maps as Standard
If your brand or business is already using interactive maps to share your data, then great. If not, you're behind the trend. Interactive maps aren't the next big thing - they're already industry standard. They're expected. People love maps, and people love interacting with them. This, of course, isn't old news for interactive map developers like us, but the popularity of these maps is! For everyone else, embrace the game-like fun of interactive cartography. Most of all, expect to see more interactive maps than ever before! Because this trend, for a lot of people, is just catching up to itself.
2. It's All About Stories
It's no surprise us humans love stories. And while we can't be sure of our AI readers' interests just yet (who knows?), it's this incorporation of stories into data viz that audiences are loving. We know as data viz creators that a data viz without context is essentially an image with a number on it or a well-formatted table at best. But context helps shape the data we're sharing and provides even more insight to allows us to tell the story we need to tell. This isn't a new concept, and in fact, it's now an expectation. Audiences are now expecting more complex data viz stories (read: more complex data) being synthesised and presented in a way that they understand and in a way that uncovers the insights 'chapter' by 'chapter'. Think more than one data viz type, a whole presentation, custom experiences, storytelling with data. Simple stories from complex data may be no easy task, but if you can get it right, you'll be leaps and bounds ahead of your competition.
3. Data Viz in Journalism
Well, the newspaper hasn't died just yet, but there's no doubt that journalism continues to evolve and change. And one change you can bet on is seeing more data viz in your paper. Why? Well, think about it. News journalism articles are getting shorter and shorter - we're busier than ever before and have attention spans equivalent to a gold fish's - yet, the same amount of information still needs to be presented in an article somehow. And, as us data viz folk know, data visualisation is a great way of doing this. Not only does data viz take up less screen-space, it also synthesizes and condenses information in a way that gives us, the reader, more time. Essentially, it's learning more in less time - it's intuitive. Editors love it, and so do readers, so editors love it even more. So, there you go. You'll be seeing more data viz than ever before while you're sipping your cup of morning coffee. Who doesn't like data viz in the morning?
4. Mobile-Friendly Data Viz
Well, it's news to nobody that we spend a lot of time on our phones. More than ever before, we're reading the news and accessing web content on our phones, so, of course, the data we're looking at should be compatible with how we're viewing it. A big part of this, given its movement into journalism (see above), is data viz. People are well and truly expecting to view great data viz on their phones, even if their screens are a lot smaller than a computer's. The reality is, without embracing the mobile audience, you're leaving a lot of your potential audience behind. So what's the solution? Well, many vendors are now working on adapting their entire desktop experiences to comfortably fit the tiny world of smartphones. And just last year, Apple acquired Mapsense, a mobile data viz startup for US$30 million. So, yeah, we're betting that making data viz 'work' on smartphones will become a basic requirement in 2018.
5. Virtual and Mixed Reality
Okay, so, picture this. Data viz. But in 3D. That's it! That's the future! We've already heard so much about how virtual and mixed reality is going to change our lives in the next however-many years, right? Well, there are a lot of people out there - we included - who are thinking this obsession with the third dimension will translate to data viz. That is, imagine how interactive, how engaging, how fun data viz could be if you could experience it? Imagine being in a virtual room and picking up and choosing the subjects and the related data sets you want to explore. We're excited for this technology ousrselves, even if it won't be this year. But one thing's for sure - we don't think Annual Report Data Viz Virtual Reality Rooms (ARDVVR) are too far off into the future. Patent pending on that name, by the way.

How Business Modernization Helps to Enable Open Data

Over the last decade, the amount of data that companies have access to has outgrown the term “big.” The volume of data at a business’ fingertips has allowed companies to take a data-driven business strategy, which helps provide transparency into their operations, workforce and customer interactions. It has become so vital that a C-level job, the chief data officer, has been created just to deal with all of it. The boom is not stopping anytime soon.
According to IDC’s “Data Age 2025: The Evolution of Data to Life-Critical”, this hyper growth of data will only continue going forward. “IDC forecasts that by 2025 the global datasphere will grow to 163 zettabytes (that is a trillion gigabytes). That’s ten times the 16.1ZB of data generated in 2016. All this data will unlock unique user experiences and a new world of business opportunities.” A major part of that new world of business opportunities is the machine learning advancements made possible by the data.
There are a few reason behind the increase in data, the first being the migration of data storage to public, private and hybrid cloud offerings. As the popularity of public infrastructure services, such as Amazon Web Services (AWS), Google’s Cloud Platform and Microsoft’s Azure, continue to grow, so does big data. These infrastructure as a service (IaaS) providers allow business to quickly spin up reliable and cost-efficient data storage. Not only does it allow for the data to sit there, but it also makes it easily accessible with the use of analytics tools. The ability for big data processing and distribution systems to consume and store unstructured data has also been an important advancement for big data.
Additionally, the growth in connected devices has contributed to the growth of the overall datasphere. The term ‘connected device’ has the connotation of a mobile phone; however, as IoT devices become more prominent in both the consumer and business world, the number of internet-enabled devices continues to skyrocket. Everything from a toaster or refrigerator, to assembly line and farming machinery may be connected to the internet, therefore constantly creating data.
This industry is poised for continued growth, so the data creation from connected devices is not slowing down. Per the IDC report, “Big Data and metadata (data about data) will eventually touch nearly every aspect of our lives — with profound consequences. By 2025, an average connected person anywhere in the world will interact with connected devices nearly 4,800 times per day — basically one interaction every 18 seconds.”
If that doesn’t sound spooky to you, then you are much braver than I am.
Outlandish statistics are hard to really comprehend, but ultimately the big data explosion will contribute and aid in the growth of machine learning. Inevitably, some of these data sets will become open-sourced because they will provide value to developers or organizations outside of those that technically own them. Large enterprises are already recognizing this, which is why Amazon, Google and government institutions, such as NASA, are making some of their data sets public. Developers will be using these big, open data sets to develop business applications for many years to come.

Machine Learning will only go as far as the Data it Learns From

Artificial intelligence (AI) is the preferred buzzword for machine learning, which is helping to rapidly progress business operations and strategy as companies embrace digital transformation. However, machine learning technology is only as good as the data it has to learn from, so businesses are attempting to harness and utilize the massive amounts of data that is accessible to them.
In 2018, businesses and developers alike will utilize not only accessible company data to train machine learning tools, but also public, open-source data sets that will have significant impact on business solutions. If a business truly understands the value of digital transformation, then it is already mining its data for actionable insights with the likes of big data analytics and business intelligence software, but it should also be using that data to train machine and deep learning models for the benefit of the company. These companies will also have to use open data sources to train AI if they want to remain on the cutting edge and further their business modernization; otherwise, they will leave themselves limited and missing opportunities to enhance their solutions with public data sets.
Big data has been a term thrown around for some time now, but it represents the massive boom in accessible data over the past decade. This boom has been made possible due to the migration of data storage to the cloud, the ability to easily scale data retention with the use of virtual machines for big data processing, and the sheer number of connected devices that create data themselves. The internet of things is only increasing the already unfathomable big data statistics and will continue to do so as that industry takes off. The ability to feed big data sets to deep learning models that contain artificial neural networks has been the reason for such rapid AI advancement (and the reason you read about it so much in the news).
Enterprise companies have already been able to take advantage of data to build machine learning tools because they have so much of it. Amazon and Google have an enormous amount of information regarding everything from the way consumers shop online to what videos people watch on YouTube, to name a few examples. With this information, these companies can build AI-driven solutions to benefit their own organizations, but also to resell as a machine learning service to outside companies — the same way they sell cloud storage. This machine learning as a service (MLaaS) is not the only benefit to third-party companies created by enterprise data, but many large businesses have opted to open source their data sets for the benefit of developers building their own business applications.
In the coming year, machine learning developers and businesses will take more advantage of open data sets from the likes of big companies, and enterprises will continue to share data that they feel could be impactful. There are a number of different data sets that provide a variety of information for machine learning tools to consume. These include image and video repositories, text data, geospatial and environmental data, transportation information and climate data. Each of these different open data sets can provide unique and critical information for developers building their own applications.
Software as a service (SaaS) vendors have been working tirelessly to add AI capabilities to their solutions, and some are beginning to prove out the benefits of machine learning features to their users. However, to take advantage of the embedded deep learning within SaaS products, businesses will have to opt in to sharing their own data with the vendor, so that the AI can best learn how to help the user and company. While businesses may be wary of this agreement (and for good reason, sharing data is scary), they will become much more open to the concept in 2018 because the benefits will outweigh the risks. These tools will be critical to digital transformation, so opening up their own data to help improve how their tools work for them will become commonplace.
In a similar vein, SaaS, cloud infrastructure and general businesses will open up data partnerships in mutually beneficial agreements to share specific data sets. Instead of hoarding valuable data, businesses will strategically — and more frequently — share data to better analyze aspects of their business, whether it is customer interactions or business operations. Many may be beneficial specifically for machine learning purposes, as businesses are building automated processes in-house and need data to progress those capabilities. Either way, the concept of open data will progress beyond the needs of developers and will lead to major business data swaps, ultimately increasing digital transformation in 2018.

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