As a Partner and Principal Consultant at Amick Brown, Arun is responsible for business development, maintaining trusted relationships with customers and providing solutions and direction. Arun served as a Solution Architect at AT&T and then Accenture with a focus on SAP ERP, Data Analytics and Technology Architecture. Arun joined Amick Brown to lead its first ever project and has since then worked with our partner companies helping to win and execute work for Amick Brown. Arun, along with the other Amick Brown partners, has been successfully working on the expansion of Amick Brown’s business into the public sector and the Cloud domain. He is also responsible for establishing and overseeing Amick Brown’s subsidiary, Amick Brown India.

Arun has all-around experience in the IT industry across functional and technical domains throughout his 24-year career. He has experience in functional areas such as Finance, Supply Chain, Human Resources and Real Estate providing support in Architecture, Strategy, Vendor Selection and ERP and Analytics Solutions. He has performed various roles covering the entire software development lifecycle and numerous technologies from the development and management of complex technology landscapes to the nitty gritty troubleshooting of technical issues.

Arun has been an avid hiker since his college days and continues even now with many hiking and backpacking trips. In 2019 he has done several hikes in the California Sierras including the feat of climbing Mount Whitney in one day. He has a keen interest in history, economics and the financial markets. He also likes to spend time on sports, cooking, winemaking and music. He is a hobby guitarist and enjoys playing rock, blues and jam music.

Arun lives in San Ramon, California, with his wife of 18 years and two children who are also budding musicians.

Follow Amick Brown at: https://www.linkedin.com/company/amick-brown-llc

Connect to Arun through  Arun’s LinkedIn Profile.  We hope to hear from you.

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Karen is an Amick Brown Co-Founder and Managing Partner. She has long history in IT, serving both in technical and leadership roles.  She spent 26 years supporting various technologies at AT&T.  Karen’s last 10 years there was spent as a Senior Technical Director where she lead a team tasked with building the enterprise SAP BI solution at AT&T—a solution which served the company’s human resources, procurement, legal, network, finance, and real estate organizations and had a diverse end-user population of more than 400,000.

In 2010, after an outsource of the AT&T SAP application to a large consulting company, Karen shifted her focus and with her business partner Anitha Brown, started Amick Brown LLC, an IT consulting and staffing company. The company, while still a small business, is enjoying steady growth and has been listed on the San Francisco Business Times Top 100 Woman Owned Businesses in the San Francisco Bay Area list since 2013.

Karen’s role at Amick Brown focuses on the internal business functions of the company and the teams supporting Human Resources, Accounting, Marketing, Legal and Finance. She is a member of the Society of Human Resource Management (SHRM) and has earned her SHRM certified HR Professional certification (SHRM-CP) which enables her to stay abreast of HR developments and ensures Amick Brown adheres to all Labor laws and regulations.  Karen ensures that business functions are running smoothly and efficiently so Amick Brown’s primary focus can remain on how to best serve both Amick Brown’s client’s and employee’s needs.

Karen lives in Livermore, California with her husband of 37 years.  She has three grown children and one “almost 3” year old grandson whom she adores.  She loves spending time with friends and family and spends as much time as possible at Lake Oroville, California where the family maintains a small houseboat.

Follow Amick Brown at: https://www.linkedin.com/company/amick-brown-llc

Connect to Karen through  Karen’s LinkedIn Profile.  Say hello, she would love to hear from you!

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We are often asked why companies would hire outside consultants, when they could likely solve their business requirements on their own. After all, they have employees in house, which is more convenient for the company to get things going on solving their problem or starting on their new project. Not only that, these employees already know the company inside and out. Why would they waste their time bringing in an outsider and have them go through onboarding, training, and acclimating them to the culture of the company. Yes, at first look these may all seem like valid points to not hire consultants. However, there are many great reasons why companies hire consultants rather than employees.

Here are some of the reason why companies hire consultants instead of employees:

1. They want someone with a broader perspective

Often times, companies or clients already have an idea on how to solve the problem they are facing, but they want to make sure that the solution they have in mind is legitimate. Sometimes, they may be close to the answer, but may be missing out due to being too close to the problem. So, they turn to consultants for their expertise because they may have already worked through a similar problem in the past with someone else. The consultant can also give insight on what they have seen work (and not work)  with prior clients. With this experience, they can bring new and innovative ideas, or possible challenges to the table. Consultants bring in a new frame of reference for the company, and helps get rid of the mentality of “things are always done this way” attitude in organizations. In doing so, solving problems or getting projects done on time become more effective.

2. They need more manpower temporarily

Companies have important problems that need solving, but they don’t necessarily have the employee breadth to focus on them. It makes it difficult, since the companies still have to focus on their everyday operations and new projects usually require reprioritizing employees’ core job responsibilities. However, hiring new employees to fill in these gaps may not always work out considering most of these projects do not last very long or may not happen at all. Despite, having employees in house, companies might have difficulties getting the teams organized to do this critical work.

In these situations, consultants come in to serve as temporary, highly skilled employees. Since, they are not full-time employees of the company, it is often cheaper to use consultants than to hire new employees. Consultants are used to switching around companies, creating a very fast learning curve. Also, companies do not have to take their own employees away from their actual day-to-day jobs. It’s a win-win situation all around!

3. They need specific skills that they do not currently have

Another reason why companies hire consultants is to acquire specific skill set that might not be easily available in house. Working with firms who have access to these highly skilled professionals may be more efficient for your company.  With constant innovation in the tech world, keeping your staff 100% state-of-the-art is nearly impossible. Luckily, consultants make it possible for companies to bring in the skill set they demand whenever they need it, since staffing firms make it their business to keep consultants trained and ready for every situation.

4. Sometimes, it is better to have a mediator/ nonpartisan influence

When companies run into a challenging problem, it can be troublesome for them to make decisions or take the necessary actions without getting caught up in emotions or politics. In order to alleviate the situation, companies bring in consultants to provide unbiased solutions to the problem. Consultants are then able to come in and ensure that the problem is being handled by an external party that is both experienced and removed from any controversies. Moreover, consultants can also serve as back-up or affirmation for a client who is attempting to carry out a new idea that might not be well-received within an organization, without any risk to their career.

There are many, many more reasons why companies hire consultants/contractors. I have only touched on a few and would love to hear your must unique reason for hiring a contractor!

With the advent of culture matching and executive oversight for every project, the reward will outweigh the risk.  Save the high dollar value of advertising, screening, interviewing, and hiring an employee by meeting requirements with consultants.

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We live, play and work in an immensely diverse world.  To classify ourselves, we align with others of the same representative group.  We categorize ourselves into numerous different groups based on race, gender, age, religion, culture, ethnic background, etc.  The list of identity groupings can be endless.

The traditional view of diversity in the corporate world has had a focus on preventing discrimination of specific minority groups – preventing exclusion.  We are experiencing a shift now….from preventing exclusion to embracing inclusion.  We are moving away from regarding diversity only as a compliance requirement, to recognizing the value of and benefiting from the various perspectives of different identify groups as a business strategy.

The new view of diversity as defined by the Society for Human Resource Management encompasses “the qualities, life experiences, personalities, education, skills, competencies and collaboration of the many different types of people who are necessary to propel an organization to success.” 

20633211 – diversity color tree finger prints illustration background set. file layered for easy manipulation and custom coloring.

Some of the benefits associated with a focus on diversity and inclusion include:

• Diverse teams that include individuals of different ages and with different backgrounds and perspectives can be more creative and innovative because the contribution and influence is more varied and therefore rich.
• Employers want the best and brightest to join their organization. You don’t know what identity group your best match might be associated with.  A company with strong diversity and inclusion goals and a diverse workforce will be attractive to high potential candidates regardless of their identity group.
• As with employees, customers will be associated with many identity groups as well. A diverse and inclusive workforce as well as a brand that represents a company’s diversity position will be helpful in attracting those customers.
• While affirmative action programs still exist to counter-balance historic discrimination, fostering a diverse workforce, and working with diversity supplies will satisfy compliance requirements – not doing so might result in missed opportunities.

At a high level, developing a business strategy to support diversity and inclusion can be approached in a similar fashion as other business strategies.

• Must have Executive commitment;
• Create a responsible party/organization to champion the effort and shepherd its development and progress;
• Perform an assessment of the current state that includes not only the demographics of the organization but also the perspectives of the employees regarding the company’s diversity;
• Evaluate the results of the assessment and determine path forward that might include hiring goals encompassing all of the dimensions of diversity, diversity supplier purchasing goals and organization leadership goals to name just a few;
• Facilitate organizational, process and any system changes required to support the strategy and goals;
• Communicate and provide training to all in the organization. Ensure the message is shared by the executive leadership to demonstrate its commitment to diversity;
• Monitor, measure and evaluate – adjust as needed over time.

Consider diversity in terms of the benefits it can bring to an organization.  Companies that expand their hiring practices to include individuals from varying backgrounds and those just entering the workforce in addition to those that are seasoned with experience will be rewarded with a rich and diverse workforce.  The brand will benefit as well, and at a minimum, the daily work life will be enriched by the many cultures, generations and viewpoints offered by a diverse group of individuals.

As a core belief in how we approach our business, Amick Brown works hard every day to promote the internal and client-facing benefits of diversity.

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In my previous Predictive blog, I introduced four main considerations that organizations need to keep in mind when they’re beginning that journey. Today, I’ll cover them in more detail.

1. How Do We Measure Business Value and Return on Investment?

An advanced analytics solution must make a measureable impact. If not, the solution doesn’t get noticed, never mind appreciated. This holds even more true, if the return on investment (ROI) can’t be realized as a significant opportunity to drive business growth or new market opportunities.

Take the example of a marketing campaign. The ROI is in having the intelligence to target the customers who are likely (if persuaded) to buy your product rather than finding customers who would have bought the product without any marketing required.

An advanced analytics solution will be short-lived if it creates a “wow” effect, but nothing else.  The solution must generate recurrent value, revenue, and business opportunities.

2. How Do We Use Advanced Analytics Effectively?

For your business, good questions to ask at the start of the journey are:

• Is the enterprise truly digital?
• Is there a single source of truth of all the data that is generated/captured by various functions of the enterprise?

These questions are important considerations. Why? Because businesses often approach advanced analytics in an ineffective manner.

Remember, advanced analytics drive value to every business function, be it marketing, finance, human resources, and so on. However, enterprise functions want often to embed advanced analytics into their business workflow and embark on advanced analytics initiatives in silos. Though there is value in doing so, the results can be underwhelming.

This is because they’re using adoptions of various technologies, methodologies, practices to address the use cases that might exists— but without an enterprise-wide vision for advanced analytics. Therefore, walls rather than bridges are built between the various functions.

The problem becomes self-perpetuating. With increasing adoption of advanced analytics solutions in various business units, the business as a whole finds it difficult to consolidate all the activities into a central initiative and have proper discipline and governance.

The solution is to create the vision and execute it across all functions— even if the pilot starts from one or two activities. The functions must agree that advanced analytics is an enterprise-wide mission. Leadership must demonstrate belief in an analytics-driven business that it is going to provide competitive advantage. In this way, advanced analytics becomes a true company asset.

3. Is Advanced Analytics Just Another Technology Project?

Advanced Analytics is not just another technology project. If considered to be a technology project, the business understands only the technical feasibility and not its business impact.

As mentioned, an advanced analytics initiative is the means by which a business gains a competitive advantage. It follows that outcomes provide the data to help make well-informed decisions.

A lesser or confined approach is a step in the wrong direction. There is no ROI associated with technology-only thinking, because no tangible results are expected as an outcome. An initiative to embrace advanced analytics must be inseparable from your business strategy.

4. Is Big Data Equal to High Quality Insight?

Big Data is not equal to high quality insight.  A traditional business approach is to think, “We’ve captured huge amounts of data, but how do we  make sense of it?” This is a wrong start.

The right approach is to start with a business question in mind. That way, you can ask if the data that you have is sufficient enough to provide the answer.

These are several pieces of the puzzle that need to be put together for one to find meaningful, actionable insights from the data. This is, after all,  the quest that we embarked on.

As we now know, advanced analytics is about business change, insight and value.

“The combination of some data and an aching desire for an answer does not ensure that a reasonable answer can be extracted from a given body of data”-Sunset salvo. The American Statistician 40 (1).

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In my first post, “The Real Business Intelligence” ,  I emphasized on the significance of Predictive Analytics in the Business Intelligence space.  Let us take a deeper look at Predictive Analytics by way of more concrete examples.

As a refresher, Predictive Analytics is a set of tools and techniques based on statistical and mathematical techniques to analyze historical data and subsequently predict the future. The basic premise is that by analyzing historical data, determining relationships, more specifically correlations between related (and sometimes seemingly unrelated) attributes and entities, one can derive significant insights into a system. These insights can be further used to make predictions.

Let’s take a look at this process step by step.

The fundamental component in Predictive Analytics is a Predictive Model, or just model. A Predictive Model is set of data points plus a series of algorithms working on that data. It attempts to capture the relationships between the data points by means of applying mathematical or statistical computations deployed as algorithms.

The output of a model is typically a single number — called a score. The score essentially is an quantitative value for a specific prediction by the model based on historical data. Higher the score, the more likely a certain behavior is predicted. Lower the score, the more likely the opposite behaviour is predicted.

Predictive models can be built for a wide variety of problems. But the most common predictive models, especially in the context of a business application, is one that predicts people’s behaviors. Predictive models are designed to predict how people behave under new circumstances, given what we know about how they behaved in the past with other known circumstances. For instance, Netflix’s movie recommendations — based on the movies that you have seen and rated highly (known circumstances) recommendations for new movies (unknown circumstances) are generated.

You will hear terms like “Machine Learning”, “Artificial Intelligence”, “Regression Analysis”, etc. While, each one is an independent area of mathematics and computing, for a Predictive Analytics suite, these are just different algorithms (computing models) that are employed in the process of predicting.

Let’s dig deeper into the concept of Scores. Let’s take two classical examples of scores generated on customers.

1. Based on the movies you have seen and rated in the past, Netflix tries to determine if you will like a new movie or not. Say for instance, a scoring of 0-10: 10 being a prediction of you absolutely loving the new movie, and 0 being a prediction of you absolute not caring about it. This type of a score is called a Probability Score. In essence, the score tells you the probability of you liking a movie.
2. Another type of score is called the Quantitative Score. Here the prediction is not the probability of whether you will like the movie or not, instead to quantitatively predict the amount of something. For instance, Life Insurance companies try to predict how long a certain customer will live based on the life choices and other circumstances of the customer.

In case of the Netflix model (Probability Score), if a customer gets a 8 (out of 10) for the likelihood of liking a particular movie, it can be rephrased as “There’s a 80% chance that the customer will like this movie, and a 20% chance that they will not like it”. Such a prediction is basing its prediction on the spread of probabilities (probability distribution). Another way of looking at this score of 8 (out of 10 is) “The customer might not absolutely love this movie (which would be a 10/10), but definitely not absolutely hate the movie (0/10). Instead the customer is more likely actually liking the movie to some extent (8/10), rather than completely disinterested (5/10).

In either case, a careful examination of this score tells us that all the system is doing is categorizing people’s behaviours into a set number of ranks. Therefore, predictive models which generate probability scores are usually called classification models. On the other hand, the quantitative scoring (predicted life expectancy of an insurance customer) is really a quantitative number. Another classic example is customer spend which is how much a customer is willing to pay for a new product or service. The actual value is reached at by means of various statistical and mathematical computations. These models are typically known as regression models.

A good predictive model might not be accurate in every single case, but given a large set of data (read target customers), the model regresses to the predicted mean of the behavior.

In the following posts, we will delve deeper into predictive algorithms, and try to gain a better understanding of how they work, and more importantly why they work, and why they are important in your corporate strategy.

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Amick Brown would sincerely like to Thank Dr, Pallath for his contribution today .

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