There have been many articles written and talks given over the last several years on abandoning the Enterprise Data Warehouse (EDW) in favor of an Enterprise Data Lake with some passionately promoting the idea and others just as passionately denying that this is achievable. In this article, I would like to take a more pragmatic approach to the case and try and lay down a process that enterprises should consider for a data management architecture.
The focus is on data lakes for enterprises, referred to as Enterprise Data Lake to distinguish it from data lakes created by internet, ad-tech or other technology companies that have different types of data and access requirements.
The Enterprise Data Warehouse
The much reviled and beleaguered Data Warehouse has been the mainstay of enterprises for over 20 years supporting business reports, dashboards and allowing analysts to understand how the business is functioning. Data Warehouses when built right provide robust security, audit and governance which is critical – especially with the increasing cyber-hacks today.
Alas – many data warehouse projects are so complex, they are never finished! Further, the strict, hierarchical governance that many IT departments created around the warehouse caused lots of frustration as business analysts and researchers cannot explore the data freely.
The Hadoop Phenomenon
When Hadoop entered the mainstream, the big attraction for business analysts and data scientists was the ability to store and access data outside the restrictive bounds of IT! This raised the exciting possibility of finding new insights into business operations, optimizing spend and finding new revenue streams.
3 Requirements for the Enterprise Data Lake
James Dixon coined the term Data Lake in 2010 to mean data flowing from a single source with the data being stored in its natural state. We have come some ways from that definition and the most common definition of a Data Lake today is a data repository for many different types and sources of data, be they structured or unstructured, internal or external, to facilitate different ways of accessing and analyzing the data. The Data Lake is built on Hadoop with the data stored in HDFS across a cluster of systems.
The 3 requirements for the Enterprise Data Lake are:
- It must collect and store data from one or more sources in its original, raw form and optionally, its various processed forms.
- It must allow flexible access to the data from different applications; for example, structured access to tables and columns as well as unstructured access to files.
- Entity and transaction data must have strong governance defined to prevent the lake from becoming a swamp.
Enterprise Data Lake Architecture
The diagram below shows an Enterprise Data Lake that ingests data from many typical systems such as CRM, ERP and other transactional systems. In addition, it is fed unstructured data from web logs, social media, IoT devices, third-party sites (such as DMP, D&B) creating a data repository. This rich data eco-system can now support combining multiple sources of data for more accurate analytics and never-before possible insights into business operations.
With technologies such as BigForce SNAP, it is possible to run existing enterprise Business Intelligence (BI) tools as well as perform exploratory analysis with visualization tools such as Tableau.
Enterprise Data Lake Governance
More importantly, the Hadoop eco-system now supports data governance through technologies like Ranger, Knox and Sentry. In combination with Kerberos, and enterprise identity management systems such as Active Directory (AD) or other LDAP frameworks, it is possible to implement strong security and governance rules. See “Implementing Hadoop Security” for details.
The Modern Enterprise Data Architecture
But what if you already have an existing EDW with hundreds of applications, some of which use complex analytics functions? How best can you leverage the EDW while also moving to a modern data architecture that allows new data sources to be integrated and empower your data scientists to integrate, enrich and analyze lots of data without the restrictions of the EDW?
A happy compromise between the data lake and data warehouse does exist and data architects and businesses have realized that it IS possible to build on the strengths of each system.
In this architecture, the data lake serves as the repository for all raw data, ingested from all the relevant data sources of an organization. Optionally, the data lake can also store cleansed and integrated data which is then also fed into the data warehouse. This way, newer BI applications can be built directly on the enterprise data lake while existing applications can continue to run on the EDW.
Data Governance in the Enterprise Data Lake
Data Governance policies for enterprise data in the EDW should also apply to the same data within the Enterprise Data Lake in most cases. Otherwise, this may lead to security holes and data inconsistencies between the two systems. If careful consideration is not given to governance, the data lake will turn into a data swamp !
However, since the data lake consists of all the raw data from operational systems as well as new data sources, it is possible to now provide data scientists and other analysts access to these data sets for new exploratory analytics.
Architecting a modern data architecture requires a thorough understanding of the requirements, existing applications and future needs and goals of the enterprise. Especially important to consider are Master data and Metadata management, governance and security as well as the right technologies.
At Orzota, we have built data lakes for a variety of businesses and have a methodology in place to ensure success. Contact us for more information.
What exactly is Artificial Intelligence?
Artificial intelligence is really starting to shape the world as we know it. The field of AI includes everything that has anything to do with the “intelligence” of a machine; and more specifically, that machine’s ability to imitate a human’s thought process and reasoning abilities.
While artificial intelligence develops programs to help solve problems, the patterns needed for solving a problem via AI is a lot different from the way a human would solve it. In a general sense, these programs that are developed are often designed to interpret, sort through, and provide insight from a vast amount of data. We want these AI programs to handle this data because it can process far more than a human brain ever could.
Four AI abilities
There are four abilities that contribute to artificial intelligence; and without them AI would not be what we expect.
Ability to sense
The first, the ability to sense, correlates directly with object recognition. In this case, object recognition is the picking out and identification of objects from different inputs such as videos and digital images. Natural Language Processing (NLP) also contributes to the ability to sense, meaning the ability to read text and make sense of it.
Ability to converse
The second, which is the ability to have a conversation, is the foundation to develop the ability to think. Predictive Analytics sums this up by identifying the likelihood of future outcomes based on historical data and algorithms (machine learning).
Ability to act
The third, the ability to act, refers to taking action based on thinking. This is also known as “Prescriptive Analytics,” and determines the best solutions/outcomes among various choices, with known parameters.
Ability to learn
The fourth and final ability, the ability to learn, includes automatically occurring self-improvements. Not only do these improvements need to happen, but we also need to understand how these improvements were made as they occur.
More than sci-fi robots
Throughout the advancement process of AI, the technology industry has made AI an essential part of its work. The advancement of this field has caused debate over whether AI is a threat to humanity or not. Artificial intelligence is NOT something to fear; and it IS more than just sci-fi robots taking over.
Of course, it’s easy to understand why some may think AI and robots are one in the same, getting some things mixed up. Pop culture can be blamed for this, because robots are often portrayed in such a way that may cause humans to worry about what exactly they may become. In reality, robots are physical machines created to carry out a specific task and artificial intelligence is used to develop programs to solve problems. When AI and robots are integrated, autonomous robots are born.
Practical uses today
Believe it or not, but artificial intelligence systems are seen every day. Interesting Engineering came up with a list of everyday applications of AI, which can be separated into two categories: consumer-focused and enterprise-focused.
Some consumer-focused applications include smart cars, video games, smart homes, and preventing heart attacks.
Examples of enterprise-focused applications are customer service, workflow automation, cybersecurity, and maintenance predictions.
With the increasing advancements in the field of artificial intelligence, we are destined to see more and more practical uses.
Orzota can help!
The Orzota BigForce Docu-AI Solution helps automate document workflows for insurance and finance use cases. It uses sophisticated AI techniques to parse documents (image files, PDFs, etc.), extracting information and key insights while providing instant search and analysis capabilities.
To find out more, please contact email@example.com.
Move to the Cloud – Benefits Of Cloud Computing
NOTE: This is a guest post by Danish Wadhwa
Cloud Computing has changed the way we use software – whether for personal or business use. The process of downloading, installing, configuring and maintaining different types of software, is eliminated with a move to the Cloud, saving businesses time and resources. Cloud has brought us to an era of increased responsiveness and efficiency.
Over the past few years, Cloud Computing has taken over and has become an essential part of our everyday life. Whether it is for updating a status on Facebook or for checking account balances on a smartphone, we use the Cloud. The Cloud is best at handling various processes in an organization. With it, individuals and businesses can plan, strategize and organize tasks within minutes. The Cloud can also keep information safe, while providing access from anywhere at any time.
Here are some of the top reasons for businesses to move to the Cloud:
Cloud based services are the best for businesses with fluctuating demands. The Cloud’s capacity can be increased or decreased according to specific requirements. Such flexibility gives businesses a real advantage over competitors. This operational ability of Cloud computing is one of the main reasons for moving to the Cloud .
Fast Data Recovery
Cloud keeps data protected, while offering data backup and recovery options in case of an emergency. A Cloud-based backup and recovery solution saves time and avoids large upfront investments as well. Further, by backing up to Cloud Servers in different geographical regions, a robust backup strategy can provide insurance against natural disasters, power outages, etc.
Automatic Software Updates
Cloud Computing servers are usually off-premises and suppliers of cloud computing make sure that all issues are resolved without impacting the end user, who can utilize the services of the Cloud without interruptions. Systems are maintained and kept up-to-date, with regular software updates that are done automatically. This leaves organizations to focus on matters more pertinent to their business, rather than their software and hardware infrastructure.
Cloud Computing simplifies various everyday operations and makes work easier: it provides access to data and the option to edit and share documents with different team members anytime, anywhere. One example is Asana, it is a cloud version of a project management tool that helps assign tasks to different team members, edit lists and keep track of progress, thus improving collaboration and coordination.
Safety becomes an important issue when you decide to store your entire data on the Cloud and this is where Cloud Computing’s high-end safety measures come into play. Although many enterprises pointed to security concerns as their number one reason for not moving to the cloud, that myth has been debunked. Today, the Cloud can be more secure than a private data center. Your data is encrypted to protect it for any kind of outage or disaster of any sort, from the process where your data is in transit to while it rests on the cloud servers. Just not that, customers can also choose to control their encryption keys if they wish to.
The “Pay as you Go” service allows you to pay according to your usage, thus helping small startups figure out what they need and expand as they grow. It also provides opportunities to various businesses to commence their ventures, regardless of available capital. Thus, the initial investment may be considerably low, allowing a company to gradually increase usage as it grows. The Cloud gives organizations access to enterprise-class technology, along with an opportunity to learn and understand the market and plan how to beat competition.
For mid-to-large enterprises, this one point can be a use time and money saver as on-premise infrastructure can take a lot of time to provision and needs to be planned for well in advance of the need for scale.
Apart from all the benefits we have discussed above, the Cloud is eco-friendly too. With the ability to change the server size according to usage, organizations only use the energy required at the moment, without leaving giant carbon footprints.
Why NOT move to the Cloud?
As technology leaders, we are challenged to make decisions that impact the organization’s growth. Our primary goals are to deliver on time and resolve problems efficiently, while staying within budget. The Cloud makes it possible to achieve these goals, with a proper plan and process in place. Moving to the Cloud can be one of those changes that an organization can make. Get the Devops Certification to benchmark your skills in Cloud Computing and understand its benefits.
During the summer of 2016, we had a high school student intern with us. He knew some Java from the Computer Science AP course but was very interested in using machine learning to predict health outcomes. We were skeptical at first – the prospect of teaching a teenager (even a very smart one) the fundamentals of ML, along with a new programming language and then have him apply it to a real data set … and all in the span of a summer internship seemed like an Herculean task. But seeing how keen he was, we decided to take him on.
Sushant Thyagaraj (that was his name) proved us wrong! He learned R within the first week, following that quickly with various ML algorithms through tutorials and sample exercises. He researched various publicly available data sets that might be suitable for his work, went through several iterations with a couple of the data sets before finally settling on predicting survival for lung cancer patients after thoracic surgery.
He continued fine tuning his results and wrote a full paper detailing his work (I should add that this last was done after school began). We are pleased to present his paper: Using Machine Learning to Predict the Post-Operative Life Expectancy of Lung Cancer Patients
The topic of data science has been on the rise within the tech industry. Often, we see techies conversing and sharing articles about data science on social media and we hear professionals discussing it as part of their business plan. By now, most of us are aware that it exists and have an inkling about what it does. But can you answer the following questions?
Do You Need a Data Scientist?
In the past, it has been known that larger, technologically advanced companies used data scientists (Facebook, LinkedIn, Google, etc.). However, we are seeing non-technology type businesses hire data scientists. For example, retailers are using data science for everything from understanding customers to managing inventory. Data science allows companies to gain insights from data in many fields and ultimately improve forecasting and decision making.
What Does a Data Scientist Do?
According to Dr. Steve Hanks, there are three major capabilities that data scientists must have: (1) They must understand that data has meaning, (2) They must understand the problem that you need to solve and how the data relates to that, and (3) They must understand the engineering.
A data scientist, in very general terms, looks at and investigates a set of data and then comes up with different ways to answer previously posed questions. Along the way, the data scientist may consider historical data analysis to develop analytical models and dashboards that provide insights and improve decision making.For example, a data scientist for a large retailer like Macy’s may look at not just past seasons’ data, but current economic and weather conditions to make predictions for their upcoming season. Retail executives make use of this to improve things such as sales, revenue, marketing strategies, advertising efforts, etc.
How Do You Build a Strong Data Science Team?
Choosing people that are aware and skilled in areas that fit your company’s need is essential. An article from Datafloq says, “The team needs to take the data and understand how it can affect different areas of the company and help those areas implement positive changes.” Not all the skills of a data scientist can be taught; it is important to have a natural affinity for data analytics, and the drive to produce beneficial insights to answer your company’s needs.Data scientists are not only computer scientists and statisticians, but must have a solid understanding of the business as well.
Should You Outsource Your Data?
Because this field of work is both complex and intimidating, there is a shortage of skilled professionals in the industry. Advanced analytics require a certain skill-set to develop and run machine learning models. Instead of spending the money and putting in the efforts to develop a team with the necessary skills internally, you can speed up your path to data science and outsource. For small-to-medium businesses, it can be cost-prohibitive to have their own data science team. There is work in the field of data engineering that must be done before a data scientist can develop models. This may not be an efficient use of resources for a small-to-medium business to hire both data engineers and data scientists.
Shanti Subramanyam, CEO at Orzota says, “Deciding to outsource reflects the core competency of your business. If you don’t have the financial resources or the capacity to focus on it, outsourcing is a faster and more efficient way to stand up a capability.”
If you’re overwhelmed by these questions, don’t be. Although the idea of data science and big data may seem complex, it is important to understand at least the basics. If you can articulate your business pain-points, it will be easier to answer these questions and find the best solutions to fit your company’s needs. Orzota is here to explain further, answer your questions, and offer services to help you feel comfortable with understanding and fulfilling your data science needs.