7 items tagged "skills"

  • 7 competences to develop to become a successful CIO

    7 competences to develop to become a successful CIO

    The days when CIOs could glide into a long-term career based solely on their technical abilities are rapidly fading.

    “It’s no longer enough for IT leaders to be tech experts,” warns Bob Hersch, a principal at Deloitte Consulting. The best-in-class CIOs of today are also business savvy, using their knowledge to embed IT as a service capability.

    “This business-centric approach integrates IT into an overall business strategy,” he adds.

    The best way any IT leader can augment his or her current technical knowledge — and strengthen their long-term career prospects — is by committing to developing the following seven essential business competences.

    1. An entrepreneurial mindset

    CIOs, regardless of their organization’s size, have to act like entrepreneurs, operating with speed, agility, and ever higher levels of passion, empathy, and creativity, advises Ram Nagappan, CIO at global investment firm BNY Mellon Pershing.

    Disruption is the new constant. “Competition is coming from all corners of the market, with fintechs and startups moving at light speed,” Nagappan says. To meet competition head on, CIOs must think like entrepreneurs and act as agents of change. “They need to constantly think about how their business could be disrupted at any point in time and how they can creatively deploy technology to get ahead of potential disruptors and future-proof the business,” he suggests.

    2. Strong leadership skills

    Leadership is a core competency that paves the way to successful technology transformation. “To truly lead, you must have business acumen in addition to technical understanding,” explains Richard Cox, CIO at media conglomerate Cox Enterprises. “Our jobs are really to leverage technology to unleash the potential of the business, and you simply have to have an understanding of the business landscape in order to exploit these opportunities.”

    Leadership is a combination of internal and external engagement. The problems CIOs face today are growing increasingly complex. The future is ambiguous, and answers are often not clear or simple. “The only way to navigate in... these uncharted waters, is to build an environment that allows people to bring ideas, perspectives, and input to solve problems,” Cox says. “Building teams that create aligned empowerment is more important today than ever.”

    Poor IT leaders often make the mistake of setting project plans, gate reviews, and delivery dates without educating the IT team on the who, what, when, and why of how the effort will help the enterprise, says Harley Bledsoe, CIO at BBB National Programs, a nonprofit organization that oversees more than a dozen industry self-regulation programs that sets standards for business advertising and privacy practices.

    “Bringing the team along on the journey as they execute on their deliverables is critical to developing an effective solution,” he explains.

    3. A consumer-oriented focus

    Technology has never been more powerful and accessible. Most employees — technical and non-technical — now have easy access to an array of sophistcated device, software, and network tools. CIOs need to ensure that workplace and work-at-home technologies at least keep pace with consumer products and services. Employees will quickly get frustrated if enterprise technology and services are more difficult to use than their home counterparts, Hersch warns. “When IT is perceived as an obstacle, the entire department is at risk,” he says.

    Shadow IT typically emerges when enterprise employees become dissatisfied with IT-provided tools. “These alternative IT capabilities diminish the CIO and IT’s role,” Hersch explains. “Over time, this can create the perception that the central IT department is an expensive and expendable infrastructure that doesn’t enable the organization for growth.”

    4. Financial acuity

    Once a CIO recognizes and understands the various factors that influence their enterprise’s finances, they can more accurately pinpoint the technology investments that promise to make the greatest impact.

    “It’s extremely likely technology can help solve any major problems or expand upon new opportunities,” says Martin Christopher, CIO of insurance provider CUNA Mutual Group. “It may be in accessing data for analytics, accelerating products to market, growing or optimizing channels, or [providing] automation and AI for better customer experiences, but inevitably there are tangible ways technology can help.”

    Christopher recommends spending time working with the enterprise finance planning and analysis (FP&A) team. “Too often, CIOs limit their focus to their own budgets and may only have a general sense of what’s causing changes to the company’s quarterly performance,” he says. “Your FP&A teams will often have the best sense of what’s happening ‘below the waterline,’ which could lead to a larger impact on company performance, positive or negative.”

    Christopher adds that business unit leaders will generally be grateful to see the CIO’s interest in what makes their business tick and how technology can help accelerate delivery of their objectives.

    For CIOs working for a regulated industry firm, such as insurance or financial services, Christopher suggests spending time with the organization’s governance, risk, and assurance (GRA) team.

    “CIOs who misunderstand the frame of external requirements their company operates within will find it difficult to honor commitments to their business partners,” he says. CIOs who aren’t fully informed on regulatory issues may also inadvertently discourage creative thinking, subconsciously fearing that the innovation may, in some way, violate a regulatory mandate.

    Bill VanCuren, senior vice president and CIO at NCR, believes that IT leaders should possess at least some formal accounting and finance education. Even more important, he adds, is maintaining a close working collaboration with the CFO team to review costs and other key financial issues.

    “You should also facilitate formal benchmarking of your IT costs and benefit tracking for comparison to best practices both within your industry and more broadly,” he recommends. “I personally participate in business case reviews to stay current on where IT investments are being positioned across the company.”

    5. Strategic thinking

    IT leaders should never stop refining their strategic reasoning abilities skills. “CIOs need to envision the future state of their business, spearhead strategies that create new products and business models, and influence change,” says Thomas Phelps, CIO at Laserfiche, an enterprise content management technology provider, and an adjunct professor at the University of Southern California Marshall School of Business. “To do this, you need a deep understanding of your business, your industry, and be willing to try bold new ideas.”

    CIOs are increasingly expected to stay ahead of existing and emerging technologies and evaluate them within the context of business goals. “They have to work more closely than ever with the CEO and every business line within the company,” BNY Mellon Pershing’s Nagappan says. “They must bring to the table their business knowledge as well as the creativity needed to deploy technology to advance business goals, and to deliver a seamless, superior client experience and greater efficiency.”

    6. A technologist’s mindset

    A technician has a basic knowledge of general technology principles and applications. A technologist, on the other hand, is someone who’s fully aware of current and emerging technologies and their impact on business operations and services.

    “It’s more than understanding technology — it’s also truly understanding business,” says Alicia Johnson, consulting principal of technology transformation at professional services firm Ernst & Young.

    A successful CIO must be able to set an enterprise’s IT direction while planning for future expansion. “To do this, they need to be transparent, exhibit strong communication skills, partner with other business units, develop a reliable team, and demonstrate a vision for the business,” Johnson explains.

    A major challenge for CIOs is knowing how to do more with less, particularly when planning budgets. “If a CIO can think logically about the direction and growth plans of a business, they’ll be able to understand available budgets and which investments are most important in reaching the overall business goals,” Johnson says. “This skill is essential, because it will help CIOs succeed when it comes to business investments, partnering with the business, communicating, setting expectations for stakeholders, and team development.”

    Being able to articulate a clearly defined future vision will also helps build trust within the IT team as well as with enterprise peers.

    7. A strong business communicator

    An IT leader must express ideas and concepts in a manner that business colleagues can easily understand. “Lose the tech-speak,” advises Seth Harris, a partner in executive search firm ON Partners.

    CIOs should speak in terms that a non-tech expert can understand and, whenever possible, use metrics that mean something to the business. “For example, don’t talk about upgrading a web platform, talk about driving revenue via ecommerce and the critical components needed to make that happen,” Harris suggests.

    Being an active listener goes hand-in-hand with strong communications skills. “To meet and exceed customer expectations, mutual understanding is critical, which can only be achieved through a strong relationship built through open and active back and forth communication,” BBB National Programs’ Bledsoe says.

    Author: John Edwards

    Source: CIO

  • 7 Personality assets required to be successful in data and tech

    7 Personality assets required to be successful in data and tech

    If you look at many of the best-known visionaries, such as Richard Branson, Elon Musk, and Steve Jobs, there are certain traits that they all have which are necessary for being successful. So this got me thinking, what are the characteristics necessary for success in the tech industry? In this blog, I’m going to explain the seven personality traits that I decided are necessary for success, starting with:

    1. Analytical capabilities

    Technology is extremely complex. If you want to be successful, you should be able to cope with complexity. Complexity not only from technical questions, but also when it comes to applying technology in an efficient and productive way.

    2. Educational foundation

    Part of the point above is your educational foundation. I am not talking so much about specific technical expertise learned at school or university, but more the general basis for understanding certain theories and relations. The ability to learn and process new information very quickly is also important. We all know that we have to learn new things continuously.

    3. Passion

    One of the most important things in the secret sauce for success is being passionate about what you do. Passion is the key driver of human activity, and if you love what you’re doing, you’ll be able to move mountains and conquer the world. If you are not passionate about what you are doing, you are doing the wrong thing.

    4. Creativity

    People often believe that if you are just analytical and smart, you’ll automatically find a good solution. But in the world of technology, there is no one single, optimal, rational solution in most cases. Creating technology is a type of art, where you have to look for creative solutions, rather than having a genius idea. History teaches us that the best inventions are born out of creativity.

    5. Curiosity

    The best technology leaders never stop being curious like children. Preserving an open mind, challenging everything and keeping your curiosity for new stuff will facilitate your personal success in a constantly changing world.

    6. Persistence

    If you are passionate, smart and creative and find yourself digging deeply into a technological problem, then you’ll definitively need persistence. Keep being persistent to analyze your problem appropriately, to find your solution, and eventually to convince others to use it.

    7. Being a networker and team player

    If you have all the other skills, you might already be successful. But, the most important booster of your success is your personal skillset. Being a good networker and team player, and having the right people in your network to turn to for support, will make the whole journey factors easier. There might be successful mavericks, but the most successful people in technology have a great set of soft skills.

    As you’ll notice, these characteristics aren’t traits that you are necessarily born with. For those who find that these characteristics don’t come naturally to them, you’ll be pleased to hear that all can be learned and adopted through hard work and practice. Anyone can be successful in tech, and by keeping these traits in mind in future, you too can ensure a long and successful career in tech.

    Author: Mathias Golombek

    Source: Dataversity

     

  • Becoming a better data scientist by improving your SQL skills

    Becoming a better data scientist by improving your SQL skills

    Learning advanced SQL skills can help data scientists effectively query their databases and unlock new insights into data relationships, resulting in more useful information.

    The skills people most often associate with data scientists are usually those "hard" technical and math skills, including statistics, probability, linear algebra, algorithm knowledge and data visualization.  They need to understand how to work with structured and unstructured data stores and use machine learning and analytics programs to extract valuable information from these stores.

    Data scientists also need to possess "soft" skills such as business and domain process knowledge, problem solving, communication and collaboration.

    These skills, combined with advanced SQL abilities, enable data scientists to extract value, information and insight from data.

    In order to unlock the full value from data, data scientists need to have a collection of tools for dealing with structured information. Many organizations still operate and rely heavily on structured enterprise data stores, data warehouses and databases. Having advanced skills to extract, manipulate and transform this data can really set data scientists apart from the pack.

    Advanced vs. beginner SQL skills for data scientists

    The common tool and language for interacting with structured data stores is the Structured Query Language (SQL), a standard, widely adopted syntax for data stores that contain schemas that define the structure of their information. SQL allows the user to query, manipulate, edit, update and retrieve data from data sources, including the relational database, an omnipresent feature of modern enterprises.

    Relational databases that utilize SQL are popular within organizations, so data scientists should have SQL knowledge at both the basic and advanced levels.

    Basic SQL skills include knowing how to extract information from data tables as well as how to insert and update those records.

    Because relational databases are often large with many columns and millions of rows, data scientists won't want to pull the entire database for most queries but rather extract only the information needed from a table. As a result, data scientists will need to know at a fundamental level how to apply conditional filters to filter and extract only the data they need.

    For most cases, the data that analysts need to work with will not live on just one database, and certainly not in a single table in that database.

    It's not uncommon for organizations to have hundreds or thousands of tables spread across hundreds or thousands of databases that were created by different groups and at different periods. Data scientists need to know how to join these multiple tables and databases together, making it easier to analyze different data sets.

    So, data scientists need to have deep knowledge of JOIN and SELECT operations in SQL as well as their impact on overall query performance.

    However, to address more complex data analytics needs, data scientists need to move beyond these basic skills and gain advanced SQL skills to enable a wider range of analytic abilities. These advanced skills enable data scientists to work more quickly and efficiently with structured databases without having to rely on data engineering team members or groups.

    Understanding advanced SQL skills can help data scientists stand out to potential employers or shine internally.

    Types of advanced SQL skills data scientists need to know

    Advanced SQL skills often mean distributing information across multiple stores, efficiently querying and combining that data for specific analytic purposes.

    Some of these skills include the following:

    Advanced and nested subqueries. Subqueries and nested queries are important to combine and link data between different sources. Combined with advanced JOIN operations, subqueries can be faster and more efficient than basic JOIN or queries because they eliminate extra steps in data extraction.

    Common table expressions. Common table expressions allow you to create a temporary table that enables temporary storage while working on large query operations. Multiple subqueries can complicate things, so table expressions help you break down your code into smaller chunks, making it easier to make sense of everything. 

    Efficient use of indexes. Indexes keep relational databases functioning effectively by setting up the system for expecting and optimizing for particular queries. Efficient use of indexes can greatly speed up performance, making data easier and faster to find. Conversely, poor use of indexing can lead to high query time and slow query performance, resulting in systems that can have runaway performance when queried at scale.

    Advanced use of date and time operations. Knowing how to manipulate date and time can come in handy, especially when working with time-series data. Advanced date operations might require knowledge of date parsing, time formats, date and time ranges, time grouping, time sorting and other activities that involve the use of timestamps and date formatting.

    Delta values. For many reasons, you may want to compare values from different periods. For example, you might want to evaluate sales from this month versus last month or sales from December this year versus December last year. You can find the difference between these numbers by running delta queries to uncover insights or trends you may not have seen otherwise.

    Ranking and sorting methods. Being able to rank and sort rows or values is necessary to help uncover key insights from data. Data analytics requirements might include ranking data by number of products or units sold, top items viewed, or top sources of purchases. Knowing advanced methods for ranking and sorting can optimize overall query time and provide accurate results.

    Query optimization. Effective data analysts spend time not only formulating queries but optimizing them for performance. This skill is incredibly important once databases grow past a certain size or are distributed across multiple sources. Knowing how to deal with complex queries and generate valuable results promptly with optimal performance is a key skill for effective data scientists.

    The value of advanced SQL skills

    The main purpose of data science is to help organizations derive value by finding information needles in data haystacks. Data scientists need to be masters at filtering, sorting and summarizing data to provide this value. Advanced SQL skills are core to providing this ability.

    Organizations are always looking to find data science unicorns who have all the skills they want and more. Knowing different ways to shape data for targeted analysis is incredibly desirable.

    For many decades, companies have stored valuable information in relational databases, including transactional data and customer data. Feeling comfortable finding, manipulating, extracting, joining or adding data to these databases will give data scientists a leg up on creating value from this data.

    As with any skill, learning advanced SQL skills will take time and practice to master. However, enterprises provide many opportunities for data scientists and data analysts to master those skills and provide more value to the organization with real-life data and business problems to solve.

    Author: Kathleen Walch

    Source: TechTarget

  • How people from different backgrounds are entering the data science field

    How people from different backgrounds are entering the data science field

    Data science careers used to be extremely selective and only those with certain types of traditional credentials were ever considered. While some might suggest that this discouraged those with hands-on experience from ever breaking into the field, it did at least help some companies glean a bit of information about potential hires. Now, however, an increasingly large number of people breaking into the field of data sciences actually aren’t themselves scientists.

    Many come from a business or technical background that has very little to do with traditional academic pursuits. What these prospects lack in classroom education they more than make up for with hands-on experience, which has put them in heavy demand when it comes to hire people for firms that need to tackle data analysis tasks on a regular basis. With 89 percent of recruiters saying that they need specialists who also have plenty of soft skills, it’s likely that a greater percentage of outside hires may make it into the data sciences field as a whole.

    Moving From One Career to Another

    The business and legal fields increasingly require employees to have strong mathematical skills, which has encouraged people to learn various types of skills that they might not otherwise have had. Potential hires who are constantly adding new skills to their personal set and practicing them are among those who are most likely to be able to land a new job in the field of data sciences in spite of the fact that they don’t normally have much in the way of tech industry experience.

    This is especially true of anyone who needs to perform analytic work in a very specific field. Law offices who want to apply analytic technology to injury claims would more than likely want to work with someone who has a background in these claims because they would be most capable with the unique challenges posed by accident suits. The same would go for those in healthcare.

    Providers have often expressed an interest in finding data analysis specialists who also understand the challenges associated with prescription side-effect reporting systems and patient confidentiality laws. By hiring someone who has worked in a medical office, organizations that are concerned with these rather unique problems posed by these issues. The same is probably true of those who work in precision manufacturing and even food services.

    By offering jobs to those who previously handled other unrelated responsibilities in these industries, some firms now say that they’re hiring well-rounded individuals who know about customer interactions as well as how to draw conclusions from visualizations. Perhaps most importantly, though, they’re putting themselves in a better position to survive any labor shortages that the data science field might be experiencing.

    Weathering Changes in the Labor Market

    While countless individuals naturally always struggle to find their dream job, the market currently seems to be in favor of those who want to transition into a more technically-oriented position. Firms that have to enlarge their IT departments might be feeling the crunch, so creating a resume might be all it takes for someone to land a new job. Since companies and NGOs have to compete for a relatively small number of prospects, it’s making sense for them to hire those who might not have otherwise even thought about working in the tech industry.

    Firms that find themselves in this position might not have been able to get anyone to fill these jobs if they didn’t do so. That’s also creating room for something of a cottage industry of data scientists.

    The Growth of Non-traditional Data Science Firms

    Companies that perform analytics on behalf of someone else are starting to become rather popular. Considering the rise of tracking-related laws, small business owners might look to them as a way to ensure compliance. Anything that they do on behalf of someone else usually has to be compliant with all of these rules per the terms of the agreed upon contract. This takes at least some of the burden off of companies that have little to no experience at all with monetizing their data and avoiding any legal troubles associated with doing so.

    While it’s likely that many of these smaller analysis offices will eventually merge together, the fact of the matter remains that they’re growing for the time being. As they do, they’ll probably create any number of additional positions for those looking to break into the data science field regardless of just how far their old careers were from the tech industry.

    Author: Philip Piletic

    Source: Smart Data Collective

  • How the skillset of data scientists will change over the next decade

    How the skillset of data scientists will change over the next decade

    AutoML is poised to turn developers into data scientists — and vice versa. Here’s how AutoML will radically change data science for the better.

    In the coming decade, the data scientist role as we know it will look very different than it does today. But don’t worry, no one is predicting lost jobs, just changed jobs.

    Data scientists will be fine — according to the Bureau of Labor Statistics, the role is still projected to grow at a higher than average clip through 2029. But advancements in technology will be the impetus for a huge shift in a data scientist’s responsibilities and in the way businesses approach analytics as a whole. And AutoML tools, which help automate the machine learning pipeline from raw data to a usable model, will lead this revolution.

    In 10 years, data scientists will have entirely different sets of skills and tools, but their function will remain the same: to serve as confident and competent technology guides that can make sense of complex data to solve business problems.

    AutoML democratizes data science

    Until recently, machine learning algorithms and processes were almost exclusively the domain of more traditional data science roles—those with formal education and advanced degrees, or working for large technology corporations. Data scientists have played an invaluable role in every part of the machine learning development spectrum. But in time, their role will become more collaborative and strategic. With tools like AutoML to automate some of their more academic skills, data scientists can focus on guiding organizations toward solutions to business problems via data.

    In many ways, this is because AutoML democratizes the effort of putting machine learning into practice. Vendors from startups to cloud hyperscalers have launched solutions easy enough for developers to use and experiment on without a large educational or experiential barrier to entry. Similarly, some AutoML applications are intuitive and simple enough that non-technical workers can try their hands at creating solutions to problems in their own departments—creating a “citizen data scientist” of sorts within organizations.

    In order to explore the possibilities these types of tools unlock for both developers and data scientists, we first have to understand the current state of data science as it relates to machine learning development. It’s easiest to understand when placed on a maturity scale.

    Smaller organizations and businesses with more traditional roles in charge of digital transformation (i.e., not classically trained data scientists) typically fall on this end of this scale. Right now, they are the biggest customers for out-of-the-box machine learning applications, which are more geared toward an audience unfamiliar with the intricacies of machine learning.

    • Pros: These turnkey applications tend to be easy to implement, and relatively cheap and easy to deploy. For smaller companies with a very specific process to automate or improve, there are likely several viable options on the market. The low barrier to entry makes these applications perfect for data scientists wading into machine learning for the first time. Because some of the applications are so intuitive, they even allow non-technical employees a chance to experiment with automation and advanced data capabilities—potentially introducing a valuable sandbox into an organization.
    • Cons: This class of machine learning applications is notoriously inflexible. While they can be easy to implement, they aren’t easily customized. As such, certain levels of accuracy may be impossible for certain applications. Additionally, these applications can be severely limited by their reliance on pretrained models and data. 

    Examples of these applications include Amazon Comprehend, Amazon Lex, and Amazon Forecast from Amazon Web Services and Azure Speech Services and Azure Language Understanding (LUIS) from Microsoft Azure. These tools are often sufficient enough for burgeoning data scientists to take the first steps in machine learning and usher their organizations further down the maturity spectrum.

    Customizable solutions with AutoML

    Organizations with large yet relatively common data sets—think customer transaction data or marketing email metrics—need more flexibility when using machine learning to solve problems. Enter AutoML. AutoML takes the steps of a manual machine learning workflow (data discovery, exploratory data analysis, hyperparameter tuning, etc.) and condenses them into a configurable stack.

    • Pros: AutoML applications allow more experiments to be run on data in a larger space. But the real superpower of AutoML is the accessibility — custom configurations can be built and inputs can be refined relatively easily. What’s more, AutoML isn’t made exclusively with data scientists as an audience. Developers can also easily tinker within the sandbox to bring machine learning elements into their own products or projects.
    • Cons: While it comes close, AutoML’s limitations mean accuracy in outputs will be difficult to perfect. Because of this, degree-holding, card carrying data scientists often look down upon applications built with the help of AutoML — even if the result is accurate enough to solve the problem at hand.

    Examples of these applications include Amazon SageMaker AutoPilot or Google Cloud AutoML. Data scientists a decade from now will undoubtedly need to be familiar with tools like these. Like a developer who is proficient in multiple programming languages, data scientists will need to have proficiency with multiple AutoML environments in order to be considered top talent.

    “Hand-rolled” and homegrown machine learning solutions 

    The largest enterprise-scale businesses and Fortune 500 companies are where most of the advanced and proprietary machine learning applications are currently being developed. Data scientists at these organizations are part of large teams perfecting machine learning algorithms using troves of historical company data, and building these applications from the ground up. Custom applications like these are only possible with considerable resources and talent, which is why the payoff and risks are so great.

    • Pros: Like any application built from scratch, custom machine learning is “state-of-the-art” and is built based on a deep understanding of the problem at hand. It’s also more accurate — if only by small margins — than AutoML and out-of-the-box machine learning solutions.
    • Cons: Getting a custom machine learning application to reach certain accuracy thresholds can be extremely difficult, and often requires heavy lifting by teams of data scientists. Additionally, custom machine learning options are the most time-consuming and most expensive to develop.

    An example of a hand-rolled machine learning solution is starting with a blank Jupyter notebook, manually importing data, and then conducting each step from exploratory data analysis through model tuning by hand. This is often achieved by writing custom code using open source machine learning frameworks such as Scikit-learn, TensorFlow, PyTorch, and many others. This approach requires a high degree of both experience and intuition, but can produce results that often outperform both turnkey machine learning services and AutoML.

    Tools like AutoML will shift data science roles and responsibilities over the next 10 years. AutoML takes the burden of developing machine learning from scratch off of data scientists, and instead puts the possibilities of machine learning technology directly in the hands of other problem solvers. With time freed up to focus on what they know—the data and the inputs themselves — data scientists a decade from now will serve as even more valuable guides for their organizations.

    Author: Eric Miller

    Source: InfoWorld

  • The requirements of a good big data architect

    The requirements of a good big data architect

    In order to be an excellent big data architect, it is essential to be a useful data architect; both these things are different. Let's take a look!

    Big data that is both structured and non-structured. While it presents many opportunities for organizations of all sizes, handling it is quite difficult and requires a specific set of skill sets.

    Big data is handled by a big data architect, which is a very specialized position. A big data architect is required to solve problems that are quite big by analyzing the data, using Hadoop, which is a data technology.

    A data analysis is required to handle database on a large scale and analyse the data in order to make the right business decision. An architect of this caliber is needed to be a strong team leader; he should have the ability to mentor people and to collaborate with different teams. It is also crucial for them to build relationships with various companies and vendors.

    The 6 Skills required to pursue a career as a big data architect

    Becoming a big data architect requires years of training. You need a wide range of competencies, which will grow over time as the field evolves. A big data architect needs to have the following skills:

    • The decision-making power for data analysis and he/she should also possess the quality of architecting the massive data.
    • Should know about machine learning as it is crucial; pattern recognition, clustering for handling data and text mining is a few essentials.
    • A person should have a keen interest and experience in programming languages and all the technologies that are latest. All kinds of JavaScript frameworks like HTML5, RESTful services, Spark, Python, Hive, Kafka, and CSS are few essential frameworks.
    • A big data architect should have the required knowledge as well as experience to handle data technologies that are latest such as; Hadoop, MapReduce, HBase, oozie, Flume, MongoDB, Cassandra and Pig.
    • Should know how to work in cloud environments and also should have the experience and knowledge of cloud computing.
    • Experience in data warehousing and mining is a compulsion.

    What are the special requirements for big data architects?

    The particular job requirements for big data architects are:

    • The ability to understand and also communicate the way by which the big data gets its business; whether it is through faster management skills or not.
    • Another requirement is the ability to work with diverse data, which is quite huge and is formed from a variety of sources.
    • Should have skills in big data tools and technologies; it includes technologies like the Hadoop, accumulo, MapReduce, Hive, HBase, panoply and redshift.

    A big data architect has to be good in a lot of things; they need to have the experience of designing and implementing.

    Start your training to become a big data architect

    In order to be an excellent big data architect, it is essential to be a useful data architect; both the things are different. A good data architect can only become a good big data architect. Data architects are the ones who create blueprints related to the management systems. The data architect is required to design, centralize, integrate and protect the company’s data source.

    Author: Sudhanshu Ahuja

    Source: Smart Data Collective

  • What is the critical competitive intelligence skill?

    Asking the right question.

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    “We hear only those questions for which we are in a position to find answers.” – (attributed to) Friedrich Nietszche 

     

    I’ve been thinking about what it is that makes competitive intelligence a unique endeavor, particularly since the activity itself has become widespread among many other positions (see post here). It’s not the analytical techniques – scenarios, war games, SWOT, benchmarking, technology forecasting, etc. are all applied by others in many different positions. It’s not the communication skills – every successful business professional aspires to be better at it. It’s not the process – over the years it seems that every competitive intelligence group has functioned differently based on their own unique situation and clientele.

    I’ve come to the (tentative) conclusion that it’s the ability to ask the right question about the issue at hand.

    No big deal, you say. Anyone with enough knowledge and understanding of the key variables of the situation can formulate the right question. But often the question you ask is predicated on your assumptions and situational biases. A marketing person will often ask a completely different question from the technical staff and the sales group. Even senior managers have individual assumptions and biases based on what they did that made them successful in the past.

    I’m positing that what makes a competitive intelligence staff person different is their ability to step outside of a typical business persona, and determine potential biases and situations where “we’ve always done it that way.” They dispassionately define the key question that needs to be answered, and then identify the range of potential answer(s) that can be pursued.

    Sure, you need the skills and techniques to take that question and come up with alternative answers that are relevant to your organization. But you can’t get the right answers until you ask the right questions. And that’s a much more unique and valuable skill.


    Source: decisionintel, February 16, 2015

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