Ideas Are Cheap, Execution Is Everything Xtuple Open Source


The enterprise resource planning (ERP) software market is large and complex. There are hundreds of vendors offering best-of-breed (i.e., stand alone) ERP applications or integrated ERP software suites. Additionally, many ERP software companies offer vertical market solutions to meet the unique requirements of specific industries, such as manufacturing, distribution, retail and others. We wrote this buyer’s guide to help organizations better understand how to select the best ERP system that suits their business needs. Here’s what we’ll cover: What Is ERP Software? An enterprise resource planning system helps organizations track information across all departments and business functions, from accounting to human resources to sales and beyond.

  1. Ideas Are Cheap Execution Is Everything Xtuple Open Source

Common ERP functionality includes:. Product and purchase planning.

Tweet with a location. You can add location information to your Tweets, such as your city or precise location, from the web and via third-party applications. 4 Tools: Tools and worksheets every ERP System buyer should use. While “cheaper total cost” was also a major factor (accounting for 20%. Business issues to be addressed, and execution of business processes. On-demand and open source ERP solutions are gradually gaining popularity. XTuple's OpenMFG.

Manufacturing and delivery planning. Inventory management. Shipping and payment.

Supply chain management. Accounting.

Marketing and sales. Customer relationship management The term 'ERP' took root in the U.S.

Around 1990 as a growing number of organizations required integration outside of—but not exclusive of—their manufacturing applications. They needed to share data from their with say, their financial accounting, customer relationship, supply chain or other applications. Enterprise planning software was introduced to describe a broader system that integrated each of these applications.

The top ERP software packages will cover the following application categories. A Comparison of Top ERP Solutions There are many popular accounting solutions on the market, and it can be hard to know what distinguishes one product from another and which is right for you. To help you better understand how the top accounting systems stack up against one another, we created a series of side-by-side product comparison pages that break down the details of what each solution offers in terms of pricing, applications, ease of use, support and more: Top SAP Comparisons Common Features of ERP Software Accounting systems help organizations manage their financial transactions. At its core, it will have a general ledger, accounts receivable, accounts payable and payroll.

Vendors often develop additional features and functionality to meet unique business and industry needs (e.g., with fund accounting). Example vendors include. Business intelligence, as a term, gained widespread adoption in the late '90’s. However, the technology has existed in some shape or form since the '60’s.

It is used to analyze and report business data to help companies make smarter business decisions. Core functions include analytics, data mining, and more. An example vendor is. A CRM application is used to manage interactions with prospects, customers, clients and/or partners. It tracks activity across all departments: marketing, sales and service. Core applications closely align with these departments. They include sales force automation, marketing automation and service and support.

CRM aims to increase customers, revenue and customer satisfaction. An example vendor is. Modern HR systems help organizations manage traditional HR activities such as personnel tracking and benefits administration, as well as new strategic HR initiatives like talent management, employee evaluation and learning management.

Example vendors include. An inventory management program helps companies track up-to-date information about their product supply. Its aim is to maintain optimum stock levels so that companies avoid depreciation of inventory and overspending, and ultimately maximize profits. There are different types of inventory programs to meet the unique requirements of different industries and companies. For example, a food distributor will have different inventory management needs than say, an apparel retailer. Sample products include.

We wouldn’t have enterprise resource software if it wasn’t for. Today, it’s at the core of many well-known ERP systems.

Other manufacturing applications and/or modules include manufacturing execution systems (MES), bill of materials (BOM), product life cycle management and more. Example vendors include,. The supply chain management (SCM) application tracks goods as they move from manufacturing facilities to distribution centers to retail stores. Common applications include: supply chain planning to adjust inventory as demand changes; supplier management to monitor performance of suppliers; warehouse management to track placement of goods within a warehouse, and others. What Type of Buyer Are You?

Jul 20, 2018 - We show how to download any video on your Mac, whether it's on Netflix, YouTube, Vimeo, iPlayer or Amazon Video. This tutorial shows you how to download YouTube videos on your PC or Mac. Step 1: Install ClipGrab. First of all, you need to install ClipGrab. Step 2: Copy the video link. Step 3: Insert the video link in ClipGrab. Step 4: Select download format and quality. Step 5: Grab that clip! Here is an article of how to download YouTube videos with six different methods, including download directly from website, with bowsers, media player,. Download video from youtube for mac.

Before and performing an ERP software comparison, you’ll need to determine what type of buyer you are. Over 90 percent of buyers fall into one of these three groups: Enterprise resource planning systems buyer. These buyers require integration of data across all departments. They want to have everything in one system and avoid the technical challenges of integrating disparate applications.

These buyers favor complete ERP solutions like SAP, Microsoft Dynamics, Infor, Epicor, Oracle and others. Best-of-breed buyers. These buyers require a single component like a standalone CRM system or a HR system. They often need greater functionality and more features than what is offered in an integrated suite. Because of the functional depth these buyers require, it's important that they spend time evaluating reviews for specialized systems instead of integrated suites. Small business buyers. A year ago analysts predicted that the average company would have 18 employees before adopting an ERP system.

Five years ago the average number was 29. Statistics aside, more and more small businesses want to leverage ERP technology for better business performance. In the past, high upfront costs and technical challenges kept many small businesses out of the market. But with a growing number of cloud options, small business buyers have a new opportunity to implement enterprise-level technology. Of course there are still on-premise or client/server options still available for small businesses.

Market Trends to Understand There are several trends playing out in the market. ERP software vendors are consolidating, adoption of software as a service (SaaS) is growing and more. Here we’ll highlight a few you should know about.

Vendor consolidation. The consolidation of ERP products isn’t necessarily a new trend. Mergers and acquisitions have always been a part of this market's history. However, the rate at which it’s taking place and the implications it has for buyers are worth mentioning. Large vendors continue to acquire niche vendors to round out their product lines, acquire excellent technology or to expand into new geographic markets. Buyers need to consider this when evaluating systems. In a worst-case scenario, their provider gets acquired, the product gets sunsetted and support and updates are no longer available.

Avoid this situation by considering a vendor’s financial and strategic viability. Adoption of software as a service. SaaS or is an appealing alternative to traditional on-premise systems. The initial investment is lower, the implementation, the user interface is familiar (it runs in a Web browser) and companies don’t need full-time IT staff to maintain servers and hardware. Most ERP vendors now offer—or have plans to offer—some kind of Web-based option. Mobile app development. Vendors have responded to rapid growth in smartphone adoption by developing mobile interfaces for their ERP software systems.

For example, Oracle already has a mobile client, so do SAP and Epicor. Social media integration. Although very much in its infancy, many ERP companies are developing social media tools to keep abreast of the bigger trend playing out. Internal tools are being developed to foster greater collaboration among employees, while integration betwen ERP programs and outside networks such as Facebook and Twitter is also taking place. Recent Events You Should Know About ACCEO launches cloud-based ERP solution. ACCEO Solutions has launched a cloud-based ERP offering that is built on the Acumatica platform and targets the Canadian ERP market. The solution is the fruit of ACCEO’s OEM partnership with Acumatica.

Acumatica and Malaysia’s Censof sign deal to bring cloud ERP to Southeast Asia.Acumatica signed a long-term agreement with Malaysian financial management software vendor Censof Holdings Berhad (Censof) in September to offer ERP solutions in commercial and public sectors across Southeast Asia. The alliance gives Acumatica a competitive edge via quick access to the market. The companies hope to leverage cloud computing infrastructure, which is gaining traction in the region despite the current low adoption rates. Anagram unveils cloud-based ERP system for UK small businesses.

Anagram Systems launched a cloud-based version of Encore, its ERP software for small businesses. The service was developed keeping in mind the needs and limited budgets of businesses having 1-150 employees. It provides small businesses affordable access to advanced and secure hosting facilities as well as the full range of ERP capabilities. Take this so we can help you identify the products that best fit your needs. What Is the FrontRunners Quadrant?

A Graphic of the Top-Rated ERP Products FrontRunners uses real reviews from real software users to highlight the top software products for North American small businesses. Our goal is to help small businesses to make more informed decisions about what software is right for them. That’s why we engineered FrontRunners. To create this report, we evaluated over 195 ERP products. Only those with the top scores for Usability and User Recommended made the cut as FrontRunners. Scores are based on reviews from real software users. What’s the Difference Between the “Small Vendor” and “Enterprise Vendor” Views?

The Different Graphics Show Different Sizes of Vendors Small and Enterprise refer to the size of the software vendor company—not necessarily the size of customers they serve. We break vendors into two groups for two reasons: It’s a more equal comparison of products, and software buyers have told us it’s helpful. To determine who’s Small and who’s Enterprise, we look at how many employees the vendors have. All products in FrontRunners, whether Enterprise or Small, are evaluated using the same process.

Each graphic shows the top 10-15 performers for each the Enterprise and Small vendor categories. You can switch views simply by clicking on the version you’d like to see (above the graphic). You can read more in the full. How Are FrontRunners Products Selected? Products Are Scored Based on User Reviews The gist is that products are scored in two areas—Usability and User Recommended—based on actual user ratings. To be considered at all, products must have at least 20 reviews published within the previous 18 months, and meet minimum user rating scores.

They also have to offer a core set of functionality—for example, ERP products have to offer financial management, HR management and inventory management. From there, user reviews dictate the Usability and User Recommended scores. Usability is plotted on the x-axis and User Recommended on the y-axis. You can download the full.

It contains individual scorecards for each product on the FrontRunners quadrant. But What if I Have More Questions? Check Out Our Additional Resources! Have questions about how to choose the right product for you?

You’re in luck! Every day, our team of advisors provides (free) customized shortlists of products to hundreds of small businesses. Simply take this to help us match you with products that meet your specific needs.

Or, talk to one of our experienced software advisors about your needs by calling (844) 687-6771—it’s quick, free, and there’s no obligation. For more information about FrontRunners, check out the following:. The “FrontRunners FAQs for Technology Providers,” linked at the top of, for detailed answers to commonly-asked questions. The complete to understand the scoring. For information on how to reference FrontRunners, check out the. Except in digital media with character limitations, the following disclaimer MUST appear with any/all FrontRunners reference(s) and graphic use: FrontRunners constitute the subjective opinions of individual end-user reviews, ratings, and data applied against a documented methodology; they neither represent the views of, nor constitute an endorsement by, Software Advice or its affiliates.

I've worked in a hedge fund in the past, where my role sounded a lot like what the author describes as a 'Data Engineer'. I would have thinkers, ie people with a lot of financial experience, come up with ideas on which datasets we want to import from which vendors, and how we should handle the 80 different types of corporate actions that are contained within this dataset.

I sometimes gave my own suggestions on how to improve upon their ideas, but for the most part, I was happy to focus on implementing their ideas, in the most clean, elegant, robust and testable manner possible. I was happy to do the 'plumbing' work of improving upon our tech stack and architecture, in order to make the entire system better functioning and easier to maintain. According to the author, I'm supposed to resent the fact that I'm a 'doer/plumber', and not a 'thinker'.

In reality, it was the opposite. Do I really want to spend my entire day reading the Bloomberg manual and figuring out which tables/columns will give us the data we want, and the nuances of what this dataset does and does not cover? Sorry, I have zero interest in doing that. I enjoy programming. I enjoy system design. I enjoy building stuff. I have zero interest in becoming an expert on how to interpret the Bloomberg symbology file.

Besides, if I ever left the financial industry and joined a tech company, that knowledge will become completely useless. Did I or anyone consider myself to be a 'menial' plumber?

I don't think so. I was getting paid hundreds of thousands of dollars, because the 'thinkers' recognized the value that I brought to the table. They appreciated that I could quickly and robustly implement the ideas that they had, and keep the system running smoothly without hiccups. They recognized anyone can do a 'good enough' job, but it's much much harder to find someone who can do a great job. And for my part, I was perfectly happy to be that guy. If you're someone who wants to expand your breadth and take on more 'thinker' responsibilities, more power to you.

But just don't forget that there are people like me out there too. There's no shame in being an excellent 'doer'. What’s funny to me is how many incompetent “thinkers” appear in meetings. Obviously, thought (even removed from implementation entirely) often has immense value. Eg, many people spent a lot of time thinking about arithmetic, linear algebra, floating point, compilers, and now I can go run whatever cool algorithm on my computer. But I continually seem to run into these people who seem borderline incompetent at anything but spewing out whatever pops into their head.

Half is nonsense, one-quarter would be actively destructive if you tried to implement it, they always seem to know everything about everything but whenever it’s something you know really well you can tell that they are very confused, etc. When I meet these people now I just think “oh, you’re one of those guys who is good at saying a lot of things” and then move on. This is where design doc should be required.

At my company we engineers are required to come up with a design doc and share with the whole org for feedback then a design meeting that takes place every week. At first I thought this was a step back because I felt it was a water fall but after writing my own design doc I quickly realized “talking is really fucking cheap.” Sitting down and write a doc that considers as many corner cases and implementation really produce high quality work.

It’s all about discipline indeed. You see that a lot with 2e people - bright people with some weaknesses or a disability. That could account for how negatively you have experienced this. Many 2e people have never really been taught good ways to handle the combination of big strengths and big weaknesses. I serve as a sounding board a lot for my oldest son and that works well, but it's not uncommon for such people to just be trying to meet their own need to process information and/or feed their ego, oblivious to how it impacts other people and not really welcoming of the feedback they really need for this to be constructive. A good sounding board doesn't just listen, they ask pertinent questions and make insightful comments that help move the thought process along.

Sometimes when I meet people like that, I'm able to direct the conversation to a more constructive back and forth of that sort. But some people just know they have this need to talk, they have a lot of baggage that makes them openly hostile to meaningful feedback and they crave validation. Anything other than praising their half-baked ideas is met with toxic reactions. In such cases, the best you may be able to do is basically make a few polite noises and then disengage as quickly as possible. True, almost everyone has baggage.

I think the difference is one of scale. For example, in grade 5 I got a C+ in fine arts. It devastated me to the point where I questioned my abilities and disengaged from schooling.

It's only been in the last few years that I've actually been able to apply myself to anything. Now, I can see that was an unreasonable reaction. At the time though, I didn't even understand what was happening. If I hadn't got a lot of help, I believe I would still be driven by my insecurities to this day and would be impossible to work with. What’s funny to me is how many incompetent “thinkers” appear in meetings.

My thought as well. I also worked in hedge funds for a long time, and I kept getting resistance from the self-proclaimed 'thinkers' to do even the most basic project management things like keeping a shared list of bugs, using version control, etc. It became clear that they simply didn't know how to use these tools, while claiming to specialize in financial modelling. This also turned out to be false, as moving on to other funds I discovered their way of seeing things was quite limited. Which I had a good suspicion of, but other people actually showed me how one could approach things. Part of that reckoning was that to be good at building financial strategies -something virtually nobody will tell you about- you need to be fairly good with writing code. Not just your Frankenstein of VBA, Excel, and Matlab, but including a fairly deep understanding of algorithms as well as common DevOps tools.

I'm wondering if anybody lives in the hypothetical perfect world scenario the author writes about. I'm at one of the larger tech companies and it's inconceivable that something like this could exist (though the churn here is extremely high - a mature shop with longstanding membership could implement the hypothetical in some form). Everything sounds nice when dreaming it up in one's head, but discounts the reality of things. You can only lead a horse to water so many times before recognizing that they just will not drink the water themselves - some people actively refuse to implement solutions, no matter how convenient the building process might be.

And then the more you burden them with things like SLAs, performance, etc., the more of shit show things become. There are some forward-moving, solid 'soft skill' analysts/data scientists that can make this happen.

But by and large they shouldn't all be held to this standard. Maybe my standards/expectations have been soured too much and I'm too pessimistic, but as a whole they're just not cut out for this kind of stuff. Which is fine - being a 'doer' is easy to begin with, and over time the more that you're able to automate as a data engineer, the more trivially easy ETL/everything else becomes.

I do this job and I'd say it's only enjoyable for now because I have complete freedom and I'm still learning. Splitting up the parts: Design architecture Wire up pipelines (once architecture is decided, this tends to declarative, connected is choosing schemas). Data science I'd definitely not want to be stuck doing stage 2 forever, would prefer 3. I think you're saying that you enjoyed a job which was some 1 and some 2. I'm sure there's someone out there who wants to wire up pipelines with no engineering and no analysis all day but I'd imagine it's a rare breed. Edit: I think the important distinction is team/company size. Doing a bit of everything as a 1 man team is challenging, if you have a team where devops/engineering/reports/tools have been chosen/built/standardized by specialist and you really are just wiring pipelines up, I think that would be tough.

On the other hand being in a small team condemns you to always be doing the same fractions of work because there's noone to hand off to. I think this diagnoses the problem well, but ignores an obvious solution.

A team of one data scientist and one engineer, completely responsible for building a model, and seeing it through into production, meeting all applicable SLAs and performance metrics. Or maybe it's two data scientists and one engineer, or one scientist and two engineers, whatever is required. The point is to have a small team you can hold completely accountable for their output. They sink or swim together, so there is no debating whether the scientists or engineers get the credit or take the blame. They are assessed by the effectiveness of the end product they produce. This is a great read, and this is a critical sentence: We are not optimizing the organization for efficiency, we are optimizing for autonomy.

Ideas are cheap execution is everything xtuple open source

Efficiency is for production pipelines where the product is thoroughly defined and production costs eat deeply into profit margin. Most software organizations have massive margins - but only if they get to the right product. Organizing people for ownership and autonomy engages their creativity, but also ensures that the org can move forward even when one side or the other falls behind.

There is nothing more soul sucking than writing, maintaining, modifying, and supporting ETL to produce data that you yourself never get to use or consume. Instead, give people end-to-end ownership of the work they produce (autonomy). I think this is more the point than “engineers shouldn’t write ETL”: the engineering-related department consuming the ETL’s output should likely be the ones writing/maintaining it. Or, perhaps more generally: don’t delegate entirely to another team if the team that cares about the result is capable of meeting their own needs.

Though this backfires sometimes for the engineering department in that then the engineers get forced into an 'inner-platform effect' problem that they instead have to build an ETL platform abstracting enough ETL abilities for their company's data scientists' skill level, yet generic enough for their company's data scientists' arbitrary questions/needs. That is its own soul-sucking experience. 'Can't we just hire people that can learn Power BI better? Why are we still writing data tools for people that think they know Access but barely know Excel?' Airflow and tools like it are probably the biggest reason for the shift. Another issue is the need to integrate different technologies requires having the skills of a software engineer.

When the landscape for tech in DE was oracle, mysql and Cognos, DE's didn't need to know about OOP or consensus algorithms. Because the landscape now includes hadoop, redshift, kafka, spark, airflow, notebooks, TiDB and lord knows what else, DE's need to have most of the skills of a software engineer to be successful. I've worked in BI (end-to-end - data modelling, reporting, ETL, etc.) for more than 10 years now across various organisations and since 'data science' became all the rage, I had the pleasure to work with a few data scientists.

From what I've seen so far, they are very good as statisticians (some of them university lecturers) but when it comes to building ETL pipelines, I don't think any of them could actually do it properly. Properly as in an ETL process which connects to various data sources, writes to logs, is repeatable, restartable and so on. It is not easy to get to know how to build a proper ETL process and it is not easy to learn how to 'do data science' correctly as well.

I see it as more productive (from my personal experience) to let the 'data engineers' do the 'data engineering' work - build data models, ETLs, etc. And let the 'data scientists' do the 'data science' work - build and fiddle with statistical models.

Just like with a 'full stack' developer, and the separation of work between 'back end' and 'front end' developers, it might be better to let each do what they do best unless you have people who can do both properly (but often it's hard to find them and they would actually be better in one area or the other). The frustration between the two camps - data 'engineers' and 'scientists' is usually due to mismanagement (distinct teams doing each bit separately, coordinated by one to many management layers) rather than suboptimal division and allocation of labour.

Small teams of two to four people which contain the correct mix of experts would benefit from the strengths of both data professional types, and would avoid the problems around syncing the effort. It’s a bad situation in your typical enterprise, but it’s even worse where I’ve spent my career: working with realtime industrial data.

I became convinced that building time series data pipipelines was a bad idea after many late nights in the office fixing fragile systems that couldn’t handle real-world complexity. As fun as it is to build with and learn new technologies, it’s a bad idea to build data pipelines unless you have a lot of resources and good leadership that can make peace between all the different people who touch the data. Unfortunately in the world of sensors and equipment there aren’t many solutions, so I started a company (at ) to save others from my years of struggle. It turns out it’s even harder to build a general time series data pipeline solution, but we’re making progress. Can't this whole thing be boiled down to 'DevOps for Data Science/Engineering'? Different parts of the org with different skillsets and cultures practicing empathy for each other by communicating interests in version-controlled code, allowing for guard-railed autonomy, which leads to business agility. Sounds about right.

Optimize for autonomy not efficiency Optimizing for efficiency without considering the cost of work in progress (WIP) (irrelevant ETL models), rework (unscalable models), or unplanned work (unscalable models that make it to production) results in company silos (data engineering, infrastructure engineering) cheering local maxima while covering their ass in the face of a business that's suffering from a long lead time. Two teams with two backlogs will accomplish work exponentially faster compared to three teams with three backlogs. It boggles my mind how books like The Phoenix Project are not required reading. We used them for a single data source at GitLab (Zendesk). Worked pretty well!

But we quickly hit a road block where what you could get via the UI wasn't all the tables available. We wound up forking it and adding to the tap. Basically all of our extractors and loaders are going to follow the Singer spec for taps and targets - it's a pretty nice model. Internally, we're using a tool called Meltano which is aimed at solving just your problem. Most of our data warehouse is coming from external business ops tools (Salesforce, Zuora, Zendesk, Marketo, etc.) and we're using dbt for transformations w/ Looker as the BI layer.

Definitely check our primary analytics repo 0 as all of our code is out in the open. Feel free to ping me if you have more questions - tmurphy at gitlab. At the company I work for, we have integrations with Stitch Data and Fivetran. Both are good and have been responsive to my needs. Neither have been perfect, so when I've noticed a problem I've had to keep on top of them to fix it.

I also maintain a few of our own ETL jobs for data sources that aren't supported. I will say that I recommend using an ETL vendor without reservation.

The nominal cost is more than made up for in the headaches you'll save yourself in creating and maintaining a homegrown ETL. I would highly highly recommend ETL as service, after adopting it recently. It substantially changes your relationship with your data sources in a really positive way. And frankly, ETL for common data sources is code that you just don't need to write. I would say that you should pilot with a few ETL vendors. We currently use Fivetran, they're fine but we've had enough burps that I cannot cold recommend them over other vendors.

I cannot for the life of me remember the details, but I think we went with them over Stitch for pricing reasons. As an undergraduate who is about to graduate with a degree in 'Data Science' this post encapsulates a lot of my worries as I move into the work world. Should I focus on being a 'thinker' a 'doer' or a 'plumber'? For the first three years I was planning on being a CS major until I was denied from the department: now the data science major is my only hope to graduate. I feel as though my programming skills are solid: but not good enough to be on any sort of fast paced infrastructure/devops team. On the flipside: I feel as though I am so far behind on stats/math knowledge that it's pointless to try and become a data scientist/analyst. I've thought about data engineering (the 'doer') as a happy compromise between the two.

However there are barely any intern or entry level data engineering positions that I can find. The ones I do find require knowledge of so many frameworks that I don't know where to start.

Additionally, I'm not even sure if data engineering even is a happy compromise, especially after reading the post. Time is ticking, and sooner or later I'm going to have to figure out what route to take, and how I want to specialize. I go to a hyper competitive university in a hyper competitive region of the country and I'm starting to feel like I'm falling behind and getting lost. If any of you older/more experienced engineers and scientist have advice or wisdom for me, I would very much appreciate it.

I did poorly on a math class that was required to declare the major. It's ironic since now that I'm in the data science major, I have to do even more math classes and less programming classes. I would love to do my own startup.

I have a few ideas floating around. But I feel like I lack the discipline to sit down every day and force myself to work on them without external deadlines/pressure. In terms of jumping into the tech industry: I understand the advice about looking for any job when starting out. It just seems that even a lot of the entry level jobs are very specialized. I think it’s better to focus on doing - especially if you’re interested in working with an earier stage company.

You’re much more versatile and if you choose the right company with an upward trajectory then you have the chance to specialize more into data science and learn model building if you want to. Also, data science seems sexy, but I find it most rewarding when you can put your own models into prod and also I think it’s useful for people to have the context around what it involves before they specialize. I’m 28, and had a lot of peers go into data science and quickly realize that it was the hype that lead them there and that they enjoy engineering more. Look for work in a different part of the country. Or maybe just the right organization that’s willing to take a chance on you.

We’re in Austin and we’ve hired smart, hardworking kids who’ve never touched the languages we use and get them contributing meaningfully in. We strive to lead the business with our output rather than to inform it I think the business hires data scientist to be informed. Not to make business decisions on their behalf.

Data scientists love working on problems that are vertically aligned with the business and make a big impact on the success of projects/organization through their efforts. They set out to optimize a certain thing or process or create something from scratch. These are point-oriented problems and their solutions tend to be as well.

They usually involve a heavy mix of business logic, reimagining of how things are done, and a healthy dose of creativity Again, I'm confused? That sounds like the data scientists should have majored in business then. If data scientists start doing that, what will all the other business folk do then?

Data scientists should just build out reports that provide valuable insights and potential patterns that can help make business decisions. The difference with prior reports engineer or data analysts or wtv, is that a data scientist is assumed to be able to generate statistical analysis or/and pattern analysis over the data. While prior, a data analyst only needed to perform basic versions of that which did not go beyond what SQL could do.

The data engineer should enable the data scientist to perform this analysis by both working with the software engineers to acquire it safely, securely, reliably and at scale. And working witj the data scientist in order to apply his statistical analysis efficiently and at scale to a possibly very large data set. Finally, he might need to work with both software engineer and data scientist to setup real time or close to real time versions of the analysis.

Ideas are cheap execution is everything xtuple open source

All result from the analysis should be presented (aka reported) to the business. The data scientist can suggest interpretations or ideas to address findings, but it's the business role to make tactical and strategic decisions about business processes and products. And if you're doing ML as part of a process, then you need a ML scientists. Say you need to build out voice recognition, or the likes. Basically comp sci or math majors with ML masters or PHDs. At least in my Org Reports are pretty much an after thought left to the data engineers (like me) to 'take this metric I've developed' and display it on the morning report.

Ideas Are Cheap Execution Is Everything Xtuple Open Source

Writing/updating a report is easiest part of my job it's the data that goes into building it that is hard translating the 'simple metric I've developed' and getting it to run in a robust automated and sane fashion is the difficult part. The complexities in my org are two fold. Firstly the infrastructure people don't get data - at all. They speak PLC's and HMI's to them it's all OPC and magic A2A messaging takes care of everything. All data is time series to them and it all goes into an historian (which is basically a giant ring buffer i.e it gets flushed periodically) anything beyond that is past their level of expertise. The data needs to be batched together the time series information has to be processed into 'event frames' - this data was all part of this sequence of conveyor belt movements for example. Then you need to link it to related events etc and archive it in some kind of sane fashion so that in six months time if there is a product defect or something like that you can trace the entire series of event frames for that particular production batch.

Secondly the people the article calls 'data scientists' (in my org these are Engineers - real ones of the Chem and Mech variety) don't know anything about databases or handling data they prototype their metrics in Matlab, Fortran, Excel and the like. You really need someone to translate their code into something sane that can be automated. Engineers are not taught to code at all. I know I studied engineering at university Fortran is the lingua franca. Code is just a way of representing mathematics. Asking these people to do all the data processing pipeline is just not going to happen.

It's not their job. They write the simulations and models they have the domain knowledge thats whats important for them to be worrying about. I just knew about Pentaho recently and it’s amazing. Sad that they just scrubbed off info about the free community version on their webpage, and to automate the jobs on the community version you need to do cron/Task Scheduler stuff outside of the app. I know it’s a limitation to make people jump ship to the paid version, but I just hope it’s integrated so I don’t have to think about setting up cron jobs to have automated ETLs and just have people responsible to create the jobs do the scheduling too.

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