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There are lots of analysts. Few good ones – and excellent ones are like hens teeth. Analysts are the interface between data and action – the insight of the organization. They are important. They can change your strategy. They can leverage and exploit your big data into smart data. They transform data to insight and let you take evidence-based action. The most cited gripe I hear about analysts working in the industry is " so what? ” and " I don't trust the data ." Why are there not more good analysts? The bottom line comes down to being ultra clear on what you want your analyst to do and the skills and behaviours required to do it. Then you need to recruit to this and deploy the principles throughout your organization. Let's call them hallmarks of a great analyst. What are the 7 magic hallmarks? Great analysts need to be inquiring. This means 'seeking stones to turn' and asking questions to help shape hypothesis to test. They don’t jump to the answers; they spend the time on the questions. They need to understand the business processes, people, and systems. Spending time observing and working with operational and enabling departments. Building credibility with business leads is key. Trust is critical. They need to have the right 'hard' skill sets, including technical, scientific, and mathematical. Understanding bias, causation, forecasting, and significance testing is a must. The appropriate balance and understanding of quantitative and qualitative methodology is a given. They need to be a top communicator. Building and influencing front-line business operations. Presentations and writing ability should not be compromised. They need to focus on context and storytelling. Deploying appropriate ways of visualization techniques. Articulating the ‘big picture.’ People connect with a story; so must your analyst. They need to drive a positive learning and improvement culture. It's all about the challenging issues, right? No, focus on finding good practice, too. They need to be scalpel sharp on connecting outcome and indicator metrics. The pinch points of delivery. This is essential for base lining and the framework for benefits realization and evaluation later on. How do you find an analyst like this? It all starts with good recruiting and hiring. Thorough tests and interviews against these tips is a must. Just because they have listed a load of experience does not make them right for the job. Put them through a tough process proportionate to what you what them to achieve. If you can't do it, find someone who can. When great analysts are in the workplace, they need to be nurtured. Help them learn the business and support a culture of innovation, but challenge their delivery and ensure they operate within these seven hallmarks for analysts. Your business depends on it. Article written by Sean Price Want more? For Job Seekers | For Employers | For Influencers
When it comes to data science, the C-Suite needs to be involved. There are a number of factors that lead to success with analytics, but without you, none of those really matter. Many executives get involved with analytics projects once they have overrun their budgets or failed to meet ROI goals. I have been called in to fix a number of derailed projects and we all say the same thing, “I wish we’d have gotten involved sooner.” You don’t need to run it, and you don’t have time to run it. The right roles for the C-Suite are strategy, oversight, and sponsorship. The wrong role is active, day-to-day management. I see a number senior executives, most often CMOs, CIOs, and CTOs, get dragged into running data science initiatives. That is a full-time job in and of itself, not one that a senior executive can focus on with everything else they are tasked with doing. What the C-Suite needs to know revolves around building a smart data strategy, insuring the goals are met and evangelizing a data-driven culture. Let’s start with the motivation and biggest question – Why? There are three key groups with expectations – customers, employees, and investors. These three drive data science adoption in business. Customers, both B2C and B2B, expect personalized, consistent experiences and responsive service across a number of channels . Those expectations are met with advanced analytics and data-driven automation. Employee expectations are rising, especially in those with highly sought after skill sets. Talent analytics shows the business how to attract, engage and retain their talent. This keeps productivity on the rise while keeping the cost of talent stable. Investors have come to expect a well-defined data strategy from executive leadership. Their opinions on company valuation, competitive standing, and future performance are impacted by the business’s ability to develop and execute that strategy. Data science isn’t an optional capability from the investor’s standpoint. Building a realistic data strategy Data science realism 101: You need a plan for responsibly collecting and securing data. The business needs a plan for building the capabilities and infrastructure to turn that data into insights to drive positive business outcomes. The plan needs to have measurable ROI that stakeholders agree upon up front. You need a campaign to teach the talent what to expect and why the migration to data driven will improve the business. Everyone needs to be accountable for their part in executing the data strategy. Only collect data that will not damage the company’s image and credibility when it is revealed that data is being collected. Only collect what the business is capable of securing because the cost of a breach is massive. Data governance is the term used to describe elements of data strategy like: Data Quality – Making sure the data collected is accurate and useful in data science initiatives Data Security – Keeping data safe from both internal and external loss Data Compliance – Handling the complex legal and ethical implications of data science Data Management – Where to keep it, how to transmit it and how to process it Data is like any other capital resource. Businesses exist to turn capital resources into revenue streams, and data is no exception. The capabilities to handle that transformation fall into two categories – infrastructure and talent. Infrastructure is where the data is physically stored and processed as well as the software tools needed to, process, and consume the insights generated from the transformation. Talent are the people who add value to data. Build this around a leadership team which is experienced in understanding business needs and how to meet those needs with data science solutions. The talent war in data science is real. While there are roughly 150,000 to 200,000 people in the world claiming to be data scientists, the top tier of talent is less than 20,000 people worldwide. One of these “unicorns” is worth the cost and effort to hire because they can produce systems on their own with high impacts to business performance. Give your HR and talent acquisition teams the flexibility and tools they need to attract and retain top talent as well as outsourcing where it makes sense. Overseeing data science without being dragged into managing it The difference between being pulled into the day-to-day operations and keeping a responsible level of oversight comes down to translating data science activities into KPIs that the C-Suite can track. The jargon surrounding data science is thick. An easy way to cut through the buzzwords is with a clear focus on ROI and business goals. Data science departments should be revenue generators and not cost centers. After the initial investment to ramp up, data science departments should be like any other – a contributor to the bottom line. Many data scientists will argue that analytics departments are different and need to be run or measured differently. In my experience, this is an argument to skirt oversight at best and an outright lie at worst. The rationales against oversight come down to a lack of trust between the data science group and leadership. Executive involvement from day one is a key to remove this barrier. Building trust allows senior executives to delegate the daily operations of what will be one of the business’s most important groups while maintaining a responsible level of oversight. The layer of translation between business goals and data science methods is also critical. Stakeholders need to be onboard with what is expected of the data science team as well as how success will be defined and reported. Evangelizing data-driven business Accountability needs to extend to other organizations, as well, the consumers of analytics. Data science teams cannot operate in a silo. To connect the team with business outcomes, they have to be integrated into each organization they support. That requires a level of accountability for turning the insights that data science provides into outcomes the business expects. The data science team cannot do that alone, and other business units will not act on insights they don’t trust. Again we come back to establishing trust, which is a key function of the C-Suite in a data-driven business. Accountability has to be a two-way street for a data-driven culture to take hold. The data science team is accountable for accurate, actionable insights. The consuming organization is accountable for making their business needs clear and acting on the insights they are handed. That does not work in companies where the teams do not build trust by working together closely. That working relationship starts with executive sponsors. Clear expectations and clear goals lead to success with data science as it does with any other organization or initiative. When your leadership team gets vocal about data-driven priorities, the organization gets on board with the changes needed to make that a reality. Article written by Vin Vashishta Want more? For Job Seekers | For Employers | For Influencers
Recently, in a coaching session with my boss, we were discussing the importance of big data. We were looking at both ends of the spectrum: a creative mind that paints by numbers in the abstract versus an analytical mind who presents flat spreadsheets. I used to aim for the middle, but my new goal is to combine the best of both worlds without the worst of either. Free-flowing creativity creates a wonderful and inclusive culture, but too many ideas that aren’t tracked for their success leads to wonky numbers. On the other hand, a sterile overly analytical approach can be off-putting and completely miss the boat on who might have potential in your organization. The purpose of this article is to address some of the advantages of where the creative mind meets the analytical mind to produce big data and how to use it to paint a picture of where we came from, where we are, and where we are going. 1. Data drives behavior. How do you know when something or someone is working? The professionals on my team have been indoctrinated into the culture of real-time numbers. Ask anyone on my team how many calls they have made, quotes they have out, or how much revenue they have closed today, and they will have a quick answer. One reason for that is the culture, the other is that those reports are built out in and available for the entire team to see. The real-time reporting builds competition among the team, builds urgency into each day, and best of all, puts the focus squarely on effort every minute of the day. Some leaders look at a day, a week, or a month. I look at a minute. An eight-hour work day is a short amount of time to get a lot done, so time management is key. Having access to the metrics and numbers both in real time and cumulatively for the month forces each professional on the team to react in real time and puts the focus on constant effort instead of the overall result. 2. Data paints a picture of challenges. As a leader of a sales team I need to know and understand the challenges of each member of the team and address them in a way that helps the rep overcome his/her obstacles (or helps them find a more fitting position). Everyone needs to be developed and deserves an opportunity, but in the last several years I have been able to use the data that I mine daily to better understand their challenges. If someone on the team quotes constantly and never closes (low closing ratio compared to the team average), they need to work on their closing techniques. If someone just cannot seem to meet their quota, the territory has to be dissected to see how much opportunity is there, what the numbers have looked like in the past, and make sure we have a reasonable expectation for that territory. If we do not, then the quota can be readjusted. Whatever challenges come to light through activity metrics, sales methodology, and basic skill assessment set can be seen clearly through the numbers. Then a plan can be implemented to improve the performance. 3. Data illustrates the value of coaching and training. Once the challenges have been made clear, big data is used to measure the impact of additional coaching/training. When a particular rep is struggling and the data shows where they are not meeting the mark (time management, metric expectations, keeping pace, etc.), that specific issue can be addressed and then tracked for improvement. When a rep is missing their daily pace consistently, the first thing I look at is their call volume. If it is low, then the first fix is upping the calls. Coaching and training should be an ongoing process for the team. When anyone finds success, it must be measured and tracked. For example, I am a huge believer in social selling, but how can that be tracked? Simple. We created a field in Salesforce to track all relationships built through social-selling avenues (i.e. LinkedIn) and then track the revenue off of those relationships. That can be broken down over a month to what percentage of clients are being closed, what percentage of revenue those deals represent in the overall number, and what the average deal size is from social selling compared to standard pipeline work. This applies to any new methodology that comes from coaching and training. 4. Data puts the spotlight on the marketing efforts. The way big data impacts marketing should be incredibly transparent. Marketing efforts from email campaigns to any of the myriad of methods we market to prospects and clients should have a clear and concise method of tracking results. Marketing dollars should not be repeatedly spent on initiatives that have little to no return. Some marketing efforts can become off-putting for prospects or clients like spamming their inboxes with mass email campaigns instead of specifically tailored campaigns for their vertical markets. We are currently tracking several different types of marketing campaigns spanning efforts from our marketing department, vendors, and individually run campaigns in each region. The impact of all of these are measured over a specific amount of time for return on investment and then either pushed forward, altered, or discontinued based on the results. 5. Data includes the leadership. Big data on display builds an inclusive transparency for the leadership. When numbers are on display through dashboards and reports, they are readily at hand when requested. Time can be spent more efficiently because of the real-time reports I have access to, so if a VP wants to know where we stand at any time on any product (broken out by region, territory, or rep), I can generate that report in under a minute. Years ago I would spend hours building Excel worksheets to illustrate productivity, growth trends, and forecasting models (and what a headache that was!). The key to strong reporting is to include every necessary data field, make them mandatory in the CRM, and then inspect them against the backend reporting to see how the numbers line up. There will be exceptions that need to be factored in (e.g. credits, back orders, fulfillment issues), but those should be accounted for in the differential. At the end of the day, living in those numbers keeps them at the front of mind. On any given day you can recall specific numbers that impress leadership and highlight your ability to understand the business. 6. Data shows you the money. The last and most important aspect of big data is, of course, the bottom line. Having access to the numbers past, present, and future illustrates the pitfalls and providence of the sales cycle. If you have a strong year, look at what made the year strong. Was it a new major account? A fully staffed team who meets their metric expectations? Those numbers help you determine where quotas should be set, how profitable each employee is, and what kind of package you should be able to offer them to keep them happy while still building the business. In the present, it is good to know the challenge of each month’s work. Having real-time numbers allows each member of the team some time to correct their course throughout the month to hit their target. In my past I have worked with a lot of sales reps who get off to a slow start and then break their backs closing out strong. With a pace and a plan, reps can have an idea of their territory’s sales cycle.  In the slower periods they can use marketing and/or responsible discounting to hit their number. For the future, the numbers give us the ability to forecast based on the past and present numbers. If we know what we did to get to where we are, we can expect that maintaining that effort will continue a steady growth pattern. Then any additional effort can be added to the mix to accelerate those numbers. Knowing your big data leads to a culture of respecting numbers and meeting expectations. Article written by Chad Dyar Image credit by Getty Images, DigitalVision, Jonathan Kitchen Want more? For Job Seekers | For Employers | For Influencers
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