13 December 2019
13 December 2019
In today’s data-driven business world, every second counts. Surviving and thriving in the landscape of tomorrow requires an agility that’s beyond human. Business leaders that augment their resources can create real-time change and shape their future success. They know it’s time to run business intelligently. Do you?
Let’s see what BI trends will change the game.
[+ or check out our infographic at the bottom of this article!]
2019 was the year of big data uptake. Not only were companies collecting and analysing data, but many were also taking the crucial step of operationalising it. Now, this is not an easy task, especially if an organisation is new to the data analytics game. Getting everyone’s buy-in is tricky, and without it, no deep or meaningful change can occur in business operations.
Leading companies have enabled this trend with the introduction of a Chief Data Officer (CDO) or Chief Analytics Officer (CAO) to their ranks. Companies that also included automation frameworks have been able to create a singular data vision.
This year, the advanced analytics market, like big data- and prescriptive analytics, has begun to level off. Vendors offering real value and differentiation will be the only ones with the staying power to sail into 2020.
In addition, Machine Learning (ML) and Artificial Intelligence (AI) analytics are becoming more aligned with traditional analytics. Organisations are leveraging ML and AI to augment – not replace – operational analytics pipelines and improve the efficiency of additional business processes.
Big data use will continue to evolve in 2020, along with the level of AI infusion to improve data usability. Let’s look at the emerging trends in BI.
Gartner predicts that by 2021, the majority of pre-built analytics reports will either be augmented or even replaced with automated insights. These analytics systems should have the capacity to recognise what information is relevant to a user and deliver those insights only.
AI plays a vital role. It will streamline the process of getting the right insights. Users will only have to input the raw data and can then rely on augmented analytics to handle the large datasets. Augmented analytics is the combination of ML and Natural Language Processing (NLP) to improve data analytics. Augmented analytics will recognise changes in patterns and draw out the insights individuals need.
The AI in any new analytics software should provide users with the intuitive analytics interface increasingly in demand. It’ll help users save time by focussing them on the data that requires their attention. In the near future, the AI might even be able to extract the right insights for us too!
A major benefit of automated insights is the money-saving aspect. Automation creates additional business value as it can handle the less complex aspect of the work traditionally performed by data analysts. It can also help make up for a lack of analytics skills in the workforce by bringing more people into the data fold.
Analytics will progress into a space where relevancy takes centre stage and users no longer need to hunt through data to find insights. Augmented analytics will deliver relevant insights to them. And potentially even do so preemptively, like Amazon- and Netflix-like capabilities that recommend other things you might also be interested in.
Having big data is no longer enough. As an executive, you need actionable insights from the data to generate actual business value, like increased efficiency or better customer engagement. A framework is a practical solution for converting large quantities of data into actionable insights. Let’s look at how to create such a structure.
Firstly, useful insights depend on the quality of the input. It’s like the adage ‘garbage in garbage out’. Mere statements that don’t provide an understanding of what prompted a change or how to replicate the same outcome are not insights.
A truly actionable insight should include these three components:
Getting the right insights is not a complicated process. Begin by collecting the right data—data that is clean, consistent and reliable. The data you collect will depend on the nature of your business and your business goals. Tools are more readily available to help clean and rank the data these days, as no data source is 100% analytics-ready from the start.
You’ll need to ask the right questions as the premise for your data analysis. Base your questions on improving a particular business process or non-optimum situation, for example. Then you’ll be ready for the next step – analysing your data with analytics tools.
Next, you can share the analytics insights with the rest of the business in a user-friendly format. This will maximise the business value of your data. Use data visualisation tools to simplify the message for all levels of data users. Finally, you’ll be ready to take action in real-time.
Modern BI tools save time and enable agile decision making. The advances in these tools are merging the two, formerly separate functions of gathering the data and drawing actionable insights. It’d be helpful to look for tools that allow insights and analytics to be embedded in your systems – this is where the actions take place, alongside the data insights that are served up.
The global data visualisation market is forecast to register a compound annual growth rate (CAGR) of more than 9% during the period 2019-2024. The growth of the market parallels the ever-increasing quantity of big data businesses are handling. Executives need a bird’s eye view of their business data to succeed in 2020 and beyond. And visualising it in rows and columns is neither practical nor efficient.
Market experts predict that small and medium businesses are likely to be the next major adopters of data visualisation tools. This will generate further demands in the field. And the available software must fulfil this cohort’s needs. With limitations such as budget and lack of skilled data scientists, software solutions must be user-friendly, intuitive and allow for customisation.
The data visualisation technology should easily support business leaders in recognising and analysing patterns in their data to bring opportunity to the fore. Specialised BI tools that provide intuitive data visualisation will enable organisations to leverage their curiosity, delve deeper into their data and drive business growth.
Modern data management solutions provide executives with analytical information to drive business success. If data is managed effectively, businesses can access real-time insights and make agile decisions to keep them on the competitive edge.
In the future, more business leaders will adopt data management tools like DataOps, a combination of DevOps and Agile methodologies, to align data with their business’ goals. It improves the quality of data management and reduces the cycle time in business operations.
A similar trend which may gain even more traction is the use of self-service solutions to minimise a user’s reliance on data analysts or IT staff to analyse data. These solutions make it quicker and easier for anyone to dig into data and extract value.
These ever-evolving BI tools help data flow more efficiently through the entire value chain. And these methods can deliver even quicker time-to-market if supported by agile project management. Dashboard and analytics projects that used to take months to complete can now be tackled in days and weeks with these advances.
Self-driving cars or robots that can read your mood are irrefutable evidence that we are in the age of artificial intelligence. Modern AI systems include elements of artificial intelligence, machine learning and deep learning.
Deep learning is a subset of machine learning. You could say it’s the next generation of machine learning as it can extract higher-level features from raw data. And over time it can improve its own performance. Deep learning uses a powerful algorithm that can handle both supervised and unsupervised learning tasks, like speech and voice recognition. It approximates how the human brain thinks and develops.
In some respects, deep learning is different from traditional analytics techniques. It lacks the same transparency. Users could still understand how the traditional solutions arrived at insights, at least on a conceptual level. With deep learning, however, there is a sense of distrust as analysts can’t see what’s happening under the hood.
Future iterations will utilise powerful algorithms to solve complex world problems. They could include capsule neural networks, a type of neural network that will closely mimic human neurons. Or deep reinforcement learning which is a mix of deep- and reinforced learning.
Artificial intelligence will also continue to evolve by mimicking how humans think and learn. In its current form, AI is known as Artificial Narrow Intelligence. This type of AI performs specific tasks well, such as product recommendation systems. The next step with AI is Artificial General Intelligence, which theoretically will be able to solve problems intelligently and mimic human intelligence. One day its learning capacity may even become indistinguishable from humans!
Image Credit: Qlik.com
Augmented analytics has become a natural part of a company’s data analytics. It allows greater access to insights and facilitates smooth data collaboration. These changes are championing BI adoption and enabling business to keep up with the evolving technology landscape.
Augmented data discovery will likely evolve in line with the Capabilities Maturity Model six stage process. From artisan level, where everything is hand-crafted, to self-service, where there’s a higher level of interaction with the data through natural language query. Then on to the deeper insights level, the early stages of augmented data management. Next up is data foundation or the second wave of augmented data management. This step is followed by collective intelligence – the system starts to learn patterns of metrics and KPIs and alerts the user when conditions require their attention. At this stage, the system predicts outcomes and recommends actions. Lastly, you get autonomous action – a wholly data-driven system run by the predictions, insight and intent of the analytics.
This process may sound complicated, yet it should be easy for users to discover and analyse data. They should be able to query data in a way that’s familiar to them. Natural query methods or interaction with data makes the discovery process more intuitive. This method will make it easier for all users to benefit from BI. Alternatively, functionality like click-to-answer engagement, where users get more information when they click on a data point, can also lead to a more natural interaction.
A step beyond natural interfaces is natural language capabilities. This technology provides additional context to the data by providing the user with a few lines of explanation and helps the user interpret the data more accurately.
Another way business can speed up BI adoption is with the automation of data management tools, like self-configuring and self-tuning processes. Solutions like these will free up analysts to handle more high-level tasks.
Modern BI will be more accessible, through, for instance, mobile apps and embedded analytics. Users will be able to enjoy its benefits wherever they are, making analytics swift, flexible and customisable.
By 2020, more than 40% of data-related tasks will be automated, Gartner predicts. Tasks like multi-variate testing and analysis or the creation of automated weekly reports requiring no human input. Software manufacturers will need to ensure their products are user-friendly and simplify analytics for the citizen data scientist.
A significant benefit of analytics automation is the democratisation of data. Data analysis in its current form often still depends on a skilled data science team, yet there is a move towards democratising data. Citizen data scientists bridge the gap between the skilled data scientist and the average self-service analytics business user. The right analytics software should enable citizen data scientists to carry out complex analysis easily using predictive or prescriptive analytics.
One common problem to avoid is the disconnect between business users and the data science process. This disconnect is the cause of many big data projects failing. It may be caused by expectations not being clear for all members of the team. Often data science projects are not tied to direct business value because of this disconnect. As a result, they may fail to provide in-depth insights that business leaders require.
Furthermore, data analysis relies on more than traditional data science skills like statistics. It is an interdisciplinary process. Domain knowledge is a critical element that should be included. This proficiency will help to extract meaningful results to feed into machine learning systems.
Data analysis automation will help companies adopt advanced analytics quicker, go from data to insights faster and drive business value.
Image Source: Gartner.com
AI is hugely beneficial to businesses. It helps improve productivity, manages resources effectively, helps us optimise time and budgets and strengthens customer relationships. It’s no wonder it’s gaining traction.
As AI becomes more infused in operational analytics, it will enable users to get better insights throughout the analytics lifecycle. The combination of powerful, future AI trends like graphs, continuous AI or explainable AI, will ensure everyone gets intuitive insights.
The worldwide investment in AI in the manufacturing sector is predicted to rocket from $2.9 billion in 2018 to $13.2 billion by 2025, according to market research firm, Tractica. The focus will be on improving operational efficiency to reduce operational costs. AI will also help improve product quality and reduce downtime.
In the media sector, AI is integral in improving customer relations. It turns customer information into targeted communication which accelerates customer engagement and enriches the customer experience. AI is contributing to new developments like augmented and virtual reality content in the industry.
Finance sector institutions must embrace AI going forward. Chatbots, for instance, are a critical tool for engaging their Millenial and Gen Z clients. And future banking applications will employ AI combined with messaging apps.
Technology innovation is not only spiking in business intelligence but also in the field of information security. Stricter regulations, like GDPR, are forcing companies that handle data to adapt to new legislation for their data collection, management and storage procedures.
Managing data security will feature high on the list of approximately 70% of organisations, according to Gartner. Though the spend in cybersecurity is predicted to reach $128 billion by 2020, the allocation to endpoint security does not prevent the 70% of breaches which stem from this same weak point. For instance, Healthcare data breaches in the USA are at an all-time high – see our Qlik Sense app on the HHS Wall of Shame.
Changes in information security are coupled with new ethics frameworks to keep people’s private information private. Advancements in encryption and multi-step verification will also contribute to a safer information future.
Image source: clevertap.com
Technology is taking on a human feel through natural language processing (NLP). It is an AI technique that trains software to process language in a way that models how humans read. This technological advance helps us interact more easily with machines using our voices.
NLP can extract insights intelligently from text- or figure-heavy data. And it will allow AI-driven technology to respond more intelligently and flexibly to language.
Potentially, by 2021, NLP and conversational analytics will boost the adoption of analytics and business intelligence from 35% to over 50% of employees, including non-technical users, Gartner predicts.
The design of apps and websites are changing. There is a shift from responsive design, which adapts the design elements to the size of the screen, to design that is more adaptive. Adaptive design refers to the practice of creating a new design specific to each device. The site or app will detect the available space and select the most appropriate design to match its specifications.
Though responsive design is quicker and involves less work, the pros of adaptive design appear to better suit the customer-centric landscape of tomorrow. Adaptive design focuses on achieving the best user experience. And the design is tailor-made for whichever interface the user is on.
And this goes beyond mobile phones and tablets – think large-screen monitors that power data-driven command centres and how the analytics provides a comprehensive view of operations for many eyes.
The more intuitive device interactions are to the user’s situation and needs, the more likely they are to survive in a customer-led marketplace and bring about change.
The focus in 2020 will shift from solely targeting the customer experience to relooking at employee experience too. Companies that focus on processes to improve the employee experience will not only have more loyal resources, but also boost the customer experience as a natural result. Happier employees lead to satisfied customers.
Businesses should make sure their data science team especially have a high level of job satisfaction as they are a scarce resource.
A great way to boost employee experience is through excellent communication in the workplace. Clear lines of communication and easy collaboration through tools, like Slack or Zoom, are crucial for keeping a widespread workforce engaged. Mobile analytics, like productivity apps, also engage and motivate employees and should be part of a company’s diverse BI and analytics strategy.
Also, by investing in employee learning and development, your employees can improve their skills and gain confidence. They’ll be able to tackle a broader scope of work and deliver to a better standard.
Not everyone is confident to use data in their role. The best route to get their buy-in is through improved data literacy.
In a recent report by the Data Literacy Project, Qlik CEO, Mike Capone explains the direct correlation between a business’ data literacy levels and performance. Only 24% of the global workforce are confident and competent in reading, analysing and arguing with data. As a result, managers must ensure they hire employees with high data literacy levels, and also provide data literacy training for the entire workforce.
Companies can improve access to insights and data for quick decision making by employing user-friendly analytics tools. Embedded analytics feature in everyday applications, like web browsers or Slack, and provide people of all data literacy levels with access to insights.
The future of data science is in the hands of a rare breed – the data scientist. Data scientists require a very particular skill set which should include a mixture of coding, statistics, databases and data preparation skills, data visualisation and machine learning. And the more specialist their skills like data translators or domain specialists, the more sought after they’ll be in the industry.
The path to successful data science in an organisation hinges on the data being accessible and actionable. As more businesses employ data experts in the roles of CDO or CIO, they’ll be establishing and ensuring a reliable data pipeline. In the near future, CDOs will begin transitioning into CEO roles. And for businesses looking to fill a new CEO post, the best line of succession would be the CDO, as they’re already focused on cross-team optimisation. A growing number of CEOs will likely have a CDO background in the next few years.
Businesses do have additional avenues to handle the shortage of skilled data science candidates. By democratising data, or making data more accessible to the general workforce, and automating repetitive data processes, business leaders can lessen the pressure of the skills vacuum. They can also reduce the dependency on data scientists by leveraging augmented analytics which streamlines insight generation for all data level users.
Looking beyond 2020, data and storage will change and become more hybrid. Companies will store data both on-premises and in the cloud. Product features will also be hybrid with some operating in the cloud and others working on-premises or both. The benefit for businesses is that they won’t have to be hitched to a single cloud vendor but can be cloud vendor agnostic.
Businesses can also expect change in product management. In the coming decade, there will be a shift from a customer-centric based hypothesis to a test-driven process. The product improvement process will change to a methodology that begins with an initial hypothesis and undergoes a series of tests to determine a conclusive effect. Then, once the hypothesis is transformed into a quantifiable insight, the product team can run with it and invest in the right improvements. The benefit—testing will become cheaper and more prolific.
In the next few years, traditional BI dashboards may transform beyond their current capacity into operating systems. Workflows will feel like one system, allowing managers to manage one asset rather than many. Users will have a unified system instead of working in various siloed platforms. This development will considerably reduce the time it takes to look for information and allow data users to focus on operative analysis and improve business value.
The collaboration of humans and machines can create exponential business growth and a future where radical change is possible at the touch of a button or the speed of a processed thought. Isn’t it time to get on board and run your business intelligently?
Vizlib’s feature-rich data visualisation products will help you get started on your data storytelling journey.
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