When we think about agriculture, utilities, and transport—we think about IoT devices. The Internet of Things is no longer an optional benefit for a tech firm. It is an essential component of operations for almost any company in any industry. With over 27.1 billion devices to be deployed across a range of industries, like healthcare, it is important to understand how IoT devices work.
IoT devices contain petabytes of data, which could contain valuable information. Given the growing importance of IoT, adopting technology that allows organisations to analyse data from the source as soon as it is generated is important. This is where edge analytics software can be a huge boon.
IoT devices make several operations, like preventative maintenance, possible. There is one drawback to them, however, and that is time. The value promised from IoT devices only converts into tangible ROI when the right analytics software can convert the data into useful findings, in real-time. Take, for example, healthcare IoT devices.
One of the benefits of these devices is their ability to detect a person’s heart rate in real-time. This information can inform doctors when patients are undergoing a serious condition, like a heart attack.
The catch is that these readings only work when there is an analytics system that can analyse and present the findings in real-time. In such cases, the conventional data analytic pipeline will not work. It won’t be possible to have data transferred along this data pipeline, stored in a data lake, and then analysed.
This is where edge analytics software can make transformative changes to the production process. Rather than transporting data to a separate storage facility and then analysing it, edge analytics allow businesses to analyse data much closer to the source.
Instead of storing data in one location, edge analytics can analyse data closer to the point of origination. This means organisations don’t have to worry about setting up a central point of storage. Instead, they can focus on building out the comprehensive IoT network they need. This also has positive implications on data collection and analysis because most organisations can eliminate latency in data collection and processing.
How will edge analytics software work? Machine learning will play a crucial role in this function. Conventionally, the analytics model collects, stores, and prepares data for analysis. After that, an algorithm is chosen. The parameters of the algorithm and the data output, however, can vary depending on the IoT network.
This is where machine learning becomes an integral component of edge analytics. Machine learning enables organisations to find the right combination of data output and algorithm because it learns as it feeds data.
This is quite transformative because it gives organisations the option to maximise efficiency in their data collection and analysis process. The development is especially crucial when you consider that most IoT networks are deployed across the cloud. Companies in different industries, like utilities and transport, will be relying on a cloud-based IoT network for most of their operations.
Most importantly, edge analytics allows for the evolution of IoT networks. By removing some of the existing constraints, we can see IoT devices pave the way for some transformative operations. For example, IoT building technology will be able to measure a variety of factors that go beyond worker productivity.
IoT devices can measure energy efficiency, worker health and safety, along with a host of other factors that can help executives determine the overall scope and scale of their employees’ well-being, both mentally and physically. While organisations are looking into their well-being by investing in various programmes, IoT devices can measure employee well-being in ways that weren’t previously possible and get a better understanding of how effective these programmes are.
As the world’s industries become more and more dependent on IoT devices, adopting the right data analytics software can give organisations the edge they need to maximise ROI on IoT devices. Edge analytics can provide the competitive advantage that organisations need.
Visit Selerity to know more about edge analytics software and other data analysis platforms.
Organisations have always trusted SAS analytics platforms to handle large volumes of data and complex functions. Machine learning has been an integral part of SAS platforms and a huge reason why SAS platforms have performed so well (along with deep learning).
Machine learning distinguishes itself from other data analytics platforms by its ability to learn from the data it analyses. It is the latest buzzword in the world of BI because of its ability to make data analytics platforms smarter and more efficient than before.
That said, it’s important to note that machine learning is not a new technology for SAS analytics applications.
Indeed, machine learning and SAS have been synonymous with each other for a few years now, so it’s important that we address how SAS uses machine learning when integrated into its platforms and as a stand-alone system.
First, a quick guide on how machine learning in SAS works.
Machine learning algorithms learn in three different ways: supervised learning, unsupervised learning, and semi-supervised learning.
Supervised learning occurs when machine learning algorithms train on labelled data and utilise logistic, regression, and gradient boosting algorithms.
Unsupervised learning is when machine learning algorithms train on unlabeled data and use several algorithms, like K-means, clustering, and PCA.
Then, there is the middle ground in semi-supervised learning, which utilises a combination of labelled data and unlabeled data with autoencoders and TSVM algorithms.
SAS machine learning algorithms add tremendous value to SAS analytics because of their ability to perform several algorithm techniques. Some of these algorithms include neural networks, regression, decision trees, random forests, and gradient boosting (And that is just scratching the surface! Machine learning can execute several other algorithms as well.).
SAS analytics integrates machine learning, utilising it for several reasons. For example, SAS Enterprise Miner uses machine learning to perform both linear and logistic regression analysis.
Meanwhile, SAS Viya uses machine learning to unify SAS platforms on multiple mediums and improve their accessibility, so that all officials can use the platform, no matter their technical skills.
Indeed, one of the reasons why SAS Viya is such a versatile platform is because it uses machine learning to deploy multiple SAS platforms. SAS Viya uses machine learning to resolve complex problems that would otherwise delay results. Moreover, SAS Viya uses other technologies like parallel processing to streamline data collection and processing.
Machine learning has been an integral part of SAS offerings for several years, both as part of SAS software and as a stand-alone offering designed to optimise data analysis even further.
In fact, machine learning algorithms can simplify the data collection and analysis process, meaning less work for data analytics professionals. For example, machine learning can expedite the creation of predictive analytics models using features like automatic code generation and reusable code snippets.
By using machine learning, SAS analytics professionals can perform operations a lot faster. For example, autotuning capabilities can help analysts build optimal data models in shorter timeframes.
Machine learning can also optimise data discovery and data finding processes to help you spend more time on insights and less time exploring data.
SAS machine learning makes data collection and analysis more manageable. This is because machine learning is more than capable of collecting and analysing both structured and unstructured data. This allows data analytics platforms to be more efficient in their data collection and analysis processes.
Additionally, analysts spend less time cleaning data and more time uncovering patterns within the data itself. This is a better use of an analysts’ time and makes them more productive.
SAS platforms have gone a long way in optimising and improving the data collection and analysis process for most organisations. This is just the tip of the iceberg. Machine learning algorithms can learn when fed data, making it the perfect tool for performing several sophisticated functions like fraud detection.
The ability to optimise pivotal procedures and even expand into new operations makes machine learning a vital aspect of SAS analytics platforms.
Visit our website to know more about SAS analytics and its value in data analysis.
Self-service data analytics is great, but it’s not without its hiccups.
A client learnt this the hard way after implementing self-service BI in their IT operations without giving much thought to a governance framework. While the company did make some gains, they have also encountered a lot of problems.
“Data quality has come under serious scrutiny,” the company representative explained. “I can share the specifics with you later, but it’s had a huge effect on our findings. The CEO is also starting to ask questions about compliance,”
While there is no denying that self-service data analytics is incredibly powerful, it is also just as important to recognise that it needs to be regulated with a data governance framework. Without that framework, your business is going to face a lot of problems. Let us explore some of the dangers of neglecting data governance and how to resolve that problem.
Neglecting data governance hurts businesses on two fronts: Revenue generation and data compliance.
Data compliance is something all organisations should be concerned with. The world’s governments are starting to wake up to the power of data and passing laws to regulate access.
The GDPR passed by the EU is the most comprehensive law on data access and usage. But there are others to consider as well, especially for companies operating in several parts of the world. For example, Australia has the Privacy Act.
To be clear, I am not trying to suggest that organisations are actively violating data laws without data governance. But, if there are no standards in place, it becomes difficult to verify if organisations are breaking compliance laws or not.
Besides the problems related to compliance, there is also the issue of business outcomes. Data governance not only helps with compliance but also improves data quality. Without data governance, your self-service data analytics platform will be processing poor quality data.
Poor quality data will severely compromise your findings, making it much harder for business people to make decisions. Data governance ensures that data is of high quality and renders more accurate readings.
By contrast, data governance ensures that data is clean and consistent across the board. Furthermore, it’s been my experience that when there is a governance framework in place, organisations inevitably get better at organising the hierarchy of roles and responsibilities around managing data.
There are several ways for organisations to set a data governance framework that ensures compliance, transparency and better business outcomes. Here are just some of the methods you can follow.
A key part of establishing a framework for governance is addressing data preparation. Organisations should look to integrate data governance with data preparation processes to meet compliance standards. Automation and web administration technologies can help tremendously in this process.
Self-service data analytics might be easy to use, but that doesn’t guarantee that everyone can use it, nor is it a guarantee that people with access will follow data governance laws. So it is important to keep everyone up to speed by providing some training sessions.
Self-service data analytics often runs the danger of being poorly coordinated. To make sure this doesn’t happen, organisations need to maintain a balance between user agility and BI standardisation. Some ways this can be done is by creating a self-service application with standard choices or offering guidance within self-service platforms.
With data analytics becoming a bigger part of the organisation, self-service data analytics would become a vital tool to generate reports and answer business-oriented questions. So, it is important to ensure that the system follows data governance laws to ensure accurate results and that organisations are not unintentionally violating data regulations.
While this might seem like an extra step in the short-run, it yields tremendous value in the long-run.
However, while self-service data analytics is crucial for modern organisations, they are not quite up to the standard of more advanced analytics systems, like machine learning and NLP. To integrate those systems into their infrastructure, organisations should consult with SAS experts, like the Selerity team.
The approach to data analysis determines the quality of findings and insight. While technology and expertise are important, there are only two-thirds of the equation. The biggest determining factor is the approach. How do organisations use technology and expertise to accomplish business objectives? Putting together the right strategic initiatives can make the biggest difference for both small and big businesses. Failure to plan properly hurts businesses because they could spend huge sums with little ROI.
What do we mean by a strategic approach to data analysis? It means setting the right business objectives so that selected technical solutions take the team one step closer to accomplishing these goals. Several companies invest in data analytics platforms (and spend thousands of dollars) to completely transform the way their business works.
In such cases, organisations always end up falling short because analytics cannot live up to these transformative expectations. Unfortunately, data analytics was not developed to make sweeping changes across an organisation overnight. What it is built to do is solve specific problems and accomplish objectives using the data generated by the organisation.
To resolve the situation, business leaders and data teams need to agree upon a common objective.
Once this objective is set, data teams can optimise data environments and analytics to generate the necessary insights. Focusing on a specific objective is an important strategic approach to data analysis because it brings data teams and business people together in mutual understanding, instead of leaving them on opposite ends of the understanding spectrum (an all too common occurrence in business).
Furthermore, it gives data analysts a solid idea of what business leaders are looking for, making it easier to map out the data analysis process and ensure each step takes the team one step closer to completing business aims. Data teams have an easier time deciding what to measure, selecting the right measuring techniques and analysis methods. It also becomes easier to achieve the transformative aspirations of the upper echelons of leadership over time.
The best approach to data analysis is maintaining a balance between centralised and decentralised practices. Over-centralising hurts productivity and efficiency, but no centralisation hurts focus. Maintaining a balance between independence and accountability can be challenging, but there are practices to aid, in this regard.
Automation tools can help with data analysis by removing mundane, unnecessary tasks from the data team’s plate, giving them the independence to be more productive. Documentation is an excellent example because data scientists need to document their work to better understand work processes. However, it is a time-consuming task, especially when the engineer has to document every new table or idea.
Automated systems make the entire process easier by creating documents on behalf of the data scientist. Data analysts still need to state the specifics of the new tables, but a large part of the documentation process is out of their hands. This means less time spent in bureaucracy and more time spent on idea generation.
Another option is to set up centralised libraries for common metrics. Be it healthcare or finance, there will be common metrics across several industries. However, there is a problem when data scientists teams create a different methodology to measure the same number.
This not only creates needless work and duplication of processes, it hurts decision-making because different methods can generate different results for the same metric. Data scientists will benefit tremendously from the use of these libraries because they can pull most of their metrics from already established libraries, while still adding additional metrics based on the client’s needs.
Choosing the best approach to data analysis is like chartering a course on a ship. The captain (business leader) can have a compass (tools) and the right crew (data science team), but all of it is for nought without the right systems and objectives in place. Hence, business leaders need to get the process right. The right process can make the difference between timely ROI and no results after years of spending. Hence, it’s important to consider the best approach to data analysis.
Is your data analysis strategy providing the most value to your client? While some SAS products, like SAS Intelligent Decisioning, provide better insight into customer interaction, there are steps in the process preventing us from unleashing the full potential of analytics. For example, is our data relevant? Are we using the latest technology? As SAS consultants, we should always be ready to examine our processes and see if we are using technology to the best of our ability, which is what I will address in this blog post.
As you collect the right data? SAS analytics programs collect data from different sources, but clients are looking to fulfil specific objectives, and they need the most relevant data to make it happen. For example, a major retailer wants to see how customers interact with their conversion funnel. Some of us would collect behavioural data to inform clients about the click and conversion rate. But what about missed opportunities? For example, what were customers looking for, but didn’t get? What were the prices that were originally quoted? All this and more can be captured through experiential data. As analysts, our data analytics strategy should always be to deliver the complete picture to our clients.
From collecting raw data for processing to producing comprehensible reports for clients to understand, SAS analysts have so many responsibilities. Hence, we need to make sure that we are using the most efficient processes to complete our work on time. Even the smallest misstep can make a huge difference in our daily work. Sticking with the example of marketing, tagging (the practice of implementing a piece of code into a page’s source, to analytics tools to connect to the server) is a fairly time-consuming process because developers have to create, test and deploy tags. The slowness in the process is further undermined by the fact that the tags need to be redeployed to accommodate website changes. As you can imagine, tagging affects our work processes by undermining speed and productivity. Reexamining these processes and seeking out alternatives will not only make our work easier but will also benefit the clients as well because we can deliver services more efficiently.
Certain industries have developed several channels to measure how customers use their services, for example, marketing and banking. But is data still operating in silos? If so, then the value of the data is completely undermined by its isolated use. Data generates the most value for organisations when it synthesises with other information from different sources to give a complete business picture. Naturally, performing such a task is not easy. However, SAS analytics products are designed to integrate data from different sources, which makes the process easier. An excellent example is SAS 360 Intelligence, which is designed to give marketers a comprehensive view of customer actions.
Having your finger on the pulse of the industry is one of the most important duties a data analyst has. The industry is always changing with new technologies used to improve what analytics can do. In the past, analytics could only describe what is happening, but now it can even predict the future in the form of predictive analytics. AI is expected to change analytics even further thanks to machine learning and natural language processing, which will allow the tech to make decisions without the need for human input. As you can imagine, this will transform how professionals operate.
SAS consultants should always examine their data analysis strategy to make sure they are providing services with the most value possible. Adjusting a strategy includes changing practices or adopting the latest technology to meet client demands. Sometimes, changing practices and incorporating technology is the same, as is the case with on-demand analytics services. On-demand services are made possible thanks to cloud technology and customer interest in analytics services, as and when they need it. On-demand consulting allows consultants, like ours, to provide data analytics services to companies in different parts of the world, making analytics more accessible and convenient than ever before.
If you are familiar with the word DevOps, then you may have heard about DataOps. The new methodology for squeezing the most value out of your data. As a data expert of more than 20 years, I have seen the industry evolve significantly in the way it collects and processes big data to keep pace with technological developments. Today, the rise of big data, the potential of IoT and advances in analytics are inspiring data analysts to seek out new methods of collecting and analysing data. Therefore, I am going to explain what exactly DataOps is and how it improves data analysis.
What is Dataops?
As mentioned before DataOps is an offshoot of DevOps. It is the latest agile operations methodology used by data professionals to improve data quality, data access, data integration, automation, deployment, data management practices, as well as the speed and accuracy of data analysis.
If you are familiar with DevOps, then you will know that it is a collaboration method between software development professionals who place focus on agility and responsiveness. DataOps is very similar, it involves the collaboration between the data professionals and is focused on making data analysis more responsive and agile. It is focused on improving and optimising all steps of the data analysis process from storage to performance optimisation.
How does data analysis improve?
DataOps provides benefits to all parties involved. Organisations that adopt this methodology have been known to outperform their competitors, which explains why the likes of Facebook and Netflix have embraced this method to execute data analysis. Thanks to DataOps, organisations can yield more data and improve the quality of data analytics. Thus, yielding better and more accurate insights.
DataOps improves strategic management practices because it encourages data scientists, data engineers and technologists to work together to gain better insights into data and improve its value. With DataOps, developers are encouraged to take on the latest technology, like machine learning because a huge part of DataOps is improving the speed and process of data analysis. This leads to more efficient processes, which means discovering new insight at a faster rate.
When DataOps is successfully implemented, it allows teams of data professionals to work faster and do more in less time. They are in a better position to respond to requests, which means they can respond to real-time goals set by the organisation.
The quality of data improves because many vital functions like data quality assurance will be automated and statistical process control (SPC). Quality improves because it even shortens the amount of time dedicated to identifying and fixing bugs or defects.
How do you setup DataOps?
Data analysis benefits tremendously from DataOps, so how do you implement a DataOps strategy? Because it involves so many steps in the analysis process, you need to implement changes across the board. It involves the democratisation of data, leveraging open source platforms and automation.
Democratise your data
Data analysis benefits from the democratisation of data. Having data in silos serves as a bottleneck for innovation and improvement. The necessity occurs because chief data officers believe that business leaders are demanding more data to aid in decision-making. In addition, providing different departments access to data encourages collaboration and access across the board, which leads to better results. An excellent example is Facebook, the social media giant was suffering a bottleneck in innovation until they moved to a different data analytics platform.
A key part of DataOps is speed and to accomplish that they need to automate many areas, like data analytics pipeline monitoring and quality assurance testing. These areas tend to take a long time and are manually intensive. Automation allows for self-sufficiency to deploy models as APIs allows engineers to integrate code without needing to restructure it, which improves productivity.
DataOps and better data analysis
DataOps refers to a new methodology for collecting, storing and analysing information. The new methodology is based on agility, responsiveness and collaboration. DataOps draws much of its inspiration from DevOps which was an agile method of software development. DataOps bring several benefits to data analysis, allowing data analysts to glean more data, accelerate the rate of work and improve the accuracy and quality of findings. To execute DataOps, organisations need to make changes to the processes across all stages. The changes consist of several steps like the democratisation of data and automation.