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Tech

The LASTING Software Big Data experience spans from pharmaceutical industry projects, to the business software and consultancy collaborations we are part of. Digital data is our field of action.

While auditing companies and being part of the preliminary consultancy stage, one can notice in time how the needs and requirements of each partner vary, even if the ultimate goals bring us all together. Staying ahead of the competition, getting a firm grasp on the industrial revolution phenomenon and keeping or making their organizations successful are all common traits for companies, even for industries.

Reaching these goals allows for a wide range of software solutions to come into play. The best solution for you is the one that provides the most suitable, actionable answer to your current needs. Being scalable is a big extra, because what you invest in now will also deliver tomorrow, the next year or in 5 years. Provided you specify this in the consultancy & design phase, a sustainable software solution should always be an integrated scope.

On this line, we ran into an article that deconstructs the Big Data needs – and we want to share a few ideas out of it with you.

 

 

Operational data, a preliminary step to Big Data

While Big Data involves a great deal of external data purchasing, operational data is internal. The ReadWrite article that caught our attention underlines the importance of harnessing this type of data, before making the leap for Big Data.

Mining data for actionable information requires attentive management, accurate analysis, and continuous adjustment, and buying software and raw data doesn’t provide companies with the skills necessary to master these processes overnight”.

In other words, the companies that are new to this need guidance. So do those that have reached the conclusion that something needs to change for the better in the way they do things.

This, as well as trying your hand at the data game by processing operational data, could both use the right partnerships.

The consultants in this field put their know-how and experience at your disposal. Once you successfully deploy operational data solutions, it may be time for big data, or not. Often the optimal solution combines both of the types of data. It all depends on the specifics of your activity.

 

The smaller-scale solutions for digital data, between creativity and customization

Let’s say your organization has a certain operational need. The answer takes the shape of a potential project. Creativity points out a certain type of solution, perhaps even a completely innovative one. When further going into the matter, some aspects become limited by technical elements. You customize down, so to say. What you imagined at a first glance can change. It happens, once confronted with budgeting, resources, compatibility, and project duration or other reasonable expectations.

When done in-house, this stage can be too harsh, or in some cases, not harsh enough. Both are dangerous, because instead of getting things done the right way, you would either settle for a more limited solution, or delay the problem solving by chasing a less realistic projection. By partnering with a software solutions company, you can test these “could be”-s against a backdrop of hands-on experience, industry know how and updated knowledge in the software solutions field.

The second stage of customization can take place when already having a certain skilled software partner. Your operational need can prove one that is familiar across your industry or field. Then the answer to it need not start from scratch. Building upon an already validated solution involves customizing up and the team creates and/or activates new features. Cost-saving and faster, this way of getting a personalized solution is perhaps the most frequent nowadays.

Contact LASTING Software, let’s talk about this and see where it takes us! We are here for your projects!

 

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News

ZDNet explores the way AI, the cloud, and Big Data in “the AI regeneration era”. The industry dubbed the adjacent infrastructure stack Industry 3.0. Due to the new generation of AI chips, data-centric software tasks should benefit in terms of both operational databases and analytics, as well as in what machine learning (ML) implies.

The easier way of handling Big Data can make companies of all sizes and from any location access new operational horizons. But organizing Big Data processing still involves a series of key decisions, which often call for experienced consultants. The pharmaceutical companies have a number of hard choices in the cloud and on-premise already laid out for them.

Bear in mind that the LASTING Software implementation of analytical algorithms and engines for statistical analysis is at the base of the world leading, FDA-approved solution. Our solution is being used by 93% of the world’s pharmaceutical companies. We will therefore walk you through the main expected pharma industry challenges, inspired by the article we mentioned.

 

The importance of processing units in accelerating your software workloads

It’s more exactly about GPUs – Graphical Processing Units, the ones that “leverage parallelism” and better keep up with Moore’s law. Their architecture responded to the new challenges well, and now one of the GPU main producers (NVIDIA), announced a set of innovative products with a new architecture.

Hardware to match the upgraded modern request is therefore on the way. But the software is also of importance. Seeing how “how GPUs are currently the AI chip of choice for ML workloads”, the ML libraries come into play.

For detailed recommendations, you may access the original article. What you need to remember is that “GPUs can greatly accelerate workloads that can be broken down in parts to be executed in parallel”. Enough said.

 

Field Programmable Gateway Arrays and their software scope

FPGAs, simplistically describable as “boards containing low-level chip fundamentals, such as AND and OR gates” are not new. Specific tasks or applications find their correspondence in the hardware description language (HDL) that specifies the FPGAs’ configuration.

Changing the said configuration at need suffers from a certain software layer immaturity. This time, the player that stands out is Intel. By investing into FPGAs R&D, this company tries to catch up on GPUs with a new line of next-gen FPGAs.

Again, the software is crucial. Along with it, the databases and libraries need to support the FPGA-accelerated analytics.  

 

Once having decided what you want, different choices ensue

To quote our inspirational article of the week: “Should you build your own infrastructure, or use the cloud? Should you wait until offerings become more mature, or jump onboard now and reap the early adopter benefits? Should you go for GPUs, or FPGAs? And then, which GPU or FPGA vendor?”

You may check some of the details and possible answers put forth by the author.

If you can make sense of them, or even get the big picture, then you must be familiar with both hardware and software – kudos to you.

Even so, the activity of your company might need the time to focus on different matters. You can still use a partnership where you can state what you need, infrastructure-wise, and your software solutions partner would deliver it.

Unable to follow the detailed options presented by ZDNet? Then the assumption that your company is a typical pharmaceutic industry entity could be the right one. No need to get stuck trying to learn software-specific notions or trying to make sense by yourself what would the best hardware elements be.

 

The multitude of available options pushes for the right partnerships. The wisest, poised for efficiency and success organizations learn to delegate tasks. To make next-gen digitization simple and get right to the point where you benefit from it, find the right software solutions partner.

We are waiting for your email or call!

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Tech

LASTING Software’s expertise in Big Data empowers you and your customers to optimize the processes & operations necessary to succeed in today’s emerging market sectors.

Remember that we always put ourselves in your clients’ shoes and deliver solutions that fit their actual needs. User experience is one differentiator that will set you apart from the competition when you partner with us.

 

The bigger the data, the more important is meeting the challenges

Harnessing Big Data may not be as easy as it seems, even in the age of hopes and technological progress we are currently experiencing. Luckily, those who paved the way in this field serve as both an example, and a cautionary tale.

Big Data is a topic of great interest – at a global level. Big companies, researchers and software engineers share their findings, and the digital community benefits from this environment. Innovation spreads around, and unproductive redundancies are less likely to take place when keeping an ear to the ground.

 

The quality of the input greatly determines the speed and accuracy of the results

 

Pete Warden, lead of the Google AI TensorFlow Mobile/Embedded team, approaches in detail the necessity of improving the training data in this post on his blog. This “garbage in, garbage out” blunt truth about Big Data is already familiar, as the online media circulates breakthroughs at a fast pace. Perhaps less important than who discovered it first is to extract the maximum of wisdom out of this nugget of tech gold.

What it means for all those developing and using Big Data solutions is that analytics accuracy depends on the quality of the input. Tailoring the criteria for input data selection is extremely important. Depending on what you need to find out when using the software solutions of choice, the processing method needs to get the right information to begin with.

As simple as it sounds – an obvious truth, perhaps, turning this into code and building apps and platforms that would select, recognize and process precisely what is relevant takes passion, experience and a restless, inquisitive mind.

 

Takeaway:

Technical details are available in the source article, so we won’t replay them here. It suffices to say that we pay attention to what the biggest players in the tech game share with the world. Their input may not come in big datasets, but in advice or recommendations, yet it holds a much-necessary relevance.

When R&D is backed up by important financing, the tech community benefits from the shared results. Our software engineers like to continually update their knowledge and expand their skills.

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