6 items tagged "Analysis"

  • 5 requirements for modern financial reporting  

    5 requirements for modern financial reporting

    How much time does your finance team spend collecting, sifting, and analyzing data?

    If you said “too much time,” you’re right. According to a Deloitte report, finance teams spend 48% of their time creating and updating reports. And when they’re operating at such a tactical level, it can be hard for them to see the forest for the trees.

    Without a modern approach to financial reporting, finance teams are so bogged down in the details that they simply don’t have the time to uncover insights in the data 一 insights that could be vital to your business.

    So how do you help them? In this piece, we’ll highlight five things you need to strengthen your financial reporting and be strategic in the data decade and how you can get them.

    1. Accountability and dynamic reports

    Finance teams have a lot riding on their shoulders. They’re responsible for reporting on business performance, something leadership teams and customers care deeply about. But business stakeholders don’t just want to be told what they want to hear. They want to know what’s really going on at the company.

    What’s happening in sales, product, marketing, customer success and how does their progress (or lack thereof) contribute to the whole? How could these groups optimize to get the most return? Extended Planning and Analysis (xP&A), or the concept of breaking down siloes and reporting across the organization, is what the future holds. But the finance department needs to change today.

    Finance teams need to be able to detect and help mitigate risk in all areas of the business. But in this day and age, there is so much noise that it’s hard to know whether inconsistencies are simply a result of bad data or if they truly represent an underlying issue that the company needs to fix. Worse, many of the reports finance teams run are in spreadsheets, which are prone to error and only show what's happening at a singlepoint in time. 

    To hold themselves and their business partners accountable, finance teams need accurate, useful financial reporting 一 they need dynamic reports. BI platforms enable finance teams’ accountability by monitoring performance, identifying trends, and determining profitability at any given moment.

    2. Transparency in business intelligence

    What was one of the most important things you learned back in high school math? Showing your work. It’s no different for finance teams, they just have to show their work on a much broader, higher stakes scale. 

    Proving that they collected the right data, used the right transformations, and performed the right analysis is finance table stakes for a company of any size. But because data is constantly growing and changing, even the basics are becoming difficult to substantiate, and will only become more difficult over time. To provide the transparency that internal and external stakeholders desire, companies need to bring their data under control. 

    Modern cloud-based solutions can integrate directly with ERPs and other accounting systems to make it abundantly clear where financial information is coming from. And the financial dashboards, budgeting tools, and forecast modeling that result show exactly what that data means for the company.

    3. Trustworthy KPIs

    It’s one thing to have a lot of data, but it’s another to actually trust those numbers. Unfortunately, most businesses, even (and perhaps especially) small ones, house their data in disparate databases, a recipe for fragmented, duplicative, and inaccurate analysis. When companies operate in this fashion, it’s no wonder stakeholders have trouble trusting their insights.

    What organizations really need is a purpose-built financial planning and reporting solution to funnel data residing in various systems into one place where it is deduped, transformed, and otherwise made ready for analysis. With a standardized, trustworthy source of truth, everyone can work under the same assumptions and draw more accurate conclusions. A single source of truth also makes your KPIs a truer reflection of where your business stands at all times.

    4. Self-service reporting

    Your finance team is probably spending their days gathering all the information they need to create and run reports, leaving them very little time to focus on strategy. In fact, McKinsey finds that finance leaders only spend 19% more time on value-add activities than other organizations, but that’s more than anyone else in their department. So how can you enable FP&A teams to actually focus on the planning and analysis?

    The answer lies in self-service reporting. Many companies rely on IT to run reports, but that can take a long time and the reports are stagnant. But what if anyone could pull their own reports? They’d get the data they need without having to wait. And everyone would have more time to surface important insights and help the company be more strategic. A self-service financial reporting software evangelizes data analysis throughout an organization, making the whole company more data-driven, productive, and effective.

    5. Data exploration with self-service reporting

    In order for finance teams to get the most out of your data, they need to break out of siloed frameworks and change their perspectives. That means they need to step away from the same models they've been leveraging over and over.

    Thinking outside the box and collaborating with other teams can reveal nuggets of wisdom that otherwise would’ve been overlooked. Financial analysis platforms can help your teams slice and dice your data and visualize it in different ways, opening the door to more creative exploration and interpretation. And when those insights are readily available, finance can share them with other teams to create and sustain a competitive edge.

    Source: Phocas Software

  • Data als ingrediënt op weg naar digitale volwassenheid

    0cd4fbcf0a4f81814f388a75109da149ca643f45Stéphane Hamel deed op 21 januari de High Tech Campus in Eindhoven aan: dé kans voor een flinke dosis inspiratie door één van ’s wereld meest vooraanstaande denkers in digital analytics. Hamel lichtte op digital maturity day 2016 (#DMD2016) het Digital Analytics Maturity-model toe.

    Imperfecte data

    Volgens Stéphane Hamel is het verschil tussen een goede en een excellente analyst het volgende: de excellente analyst weet ook bij imperfecte data te komen tot beslissingen of zinvol advies. “Data will never be perfect, know how bad the data is is essential. If you know 5 or 10% is bad, there is no problem”, aldus Hamel.

    Analytics = Context + Data + Creativity

    Analytics klinkt als een vakgebied voor datageeks en nerds. Dat beeld klopt niet: buiten de data is het onderkennen van de context waarbinnen de data zijn verzameld en creativiteit bij het interpreteren ervan essentieel. Om data te begrijpen moet je vanachter je laptop of PC vandaan komen. Alleen door de wereld ‘daarbuiten’ mee te nemen in je analyse kun je als data-analist tot zinvolle inzichten en aanbevelingen komen.

    Hamel geeft een voorbeeld uit de collegebanken: toen een groep studenten de dataset van Save the Children uit 2010 te zien kreeg, dachten sommigen dat de factor 10 toename in websiteverkeer te danken was aan een campagne of toeval. De werkelijke oorzaak was de aardbeving in Haïti.

    Digital Maturity Assessment

    Het Digital Maturity Assessment-model is ontwikkeld aan de hand van de digitale transformatie van honderden bedrijven wereldwijd. Op basis van deze ervaringen weet Stéphane welke uitdagingen bedrijven moeten overwinnen op weg naar digital leadership.

    Digital Analytics Maturity SHamel

    Dit model kun je natuurlijk gebruiken om de eigen organisatie te benchmarken tegen andere bedrijven. De meerwaarde volgens Hamel zit echter in het ‘benchmarken van jezelf versus jezelf’. Het helpt kortom om het gesprek intern aan te gaan. Als je voor de derde keer van tooling switcht, ben je zelf het probleem, niet de technologie.

    Hamel geeft de voorkeur aan een consistente score op de vijf criteria van dit Digital Maturity Assessment-model: liever een twee overall dan uitschieters naar boven of beneden. De factor die meestal het zwakst scoort is ‘process’.

    Dit criterium staat voor de werkwijze om te komen tot dataverzameling, -analyse en -interpretatie. Vaak zit dit proces zelf helemaal niet zo slecht in elkaar, maar worstelen data-analisten om aan collega’s of het managementteam uit te leggen welke stappen ze hebben gezet. Hamel benadrukt daarom: “you need a digital culture, not a digital strategy”.

    Omhels de jongens van IT

    Geef IT de kans om jou echt te helpen. Niet door te zeggen ‘voer dit uit of fix dat’. Wel door IT te vragen om samen met jullie een probleem op te lossen. Hamel ziet digitale analisten daarom vooral als change-agents, niet als stoffige dataprofessionals. Juist die shift in benadering en rol betekent dat we binnenkort niet meer spreken over digital analytics, maar over ‘analytics’.

    Data is the raw material of my craft

    Hamel’s favoriete motto “data is the raw material of my craft” verwijst naar het vakmanschap en de passie die Stéphane Hamel graag aan het vakgebied digital analytics toevoegt. Stéphane’s honger om het verschil te maken in digital analytics werd ooit tijdens een directievergadering aangewakkerd. Hamel zat in die vergadering erbij als de ‘IT guy’ en werd niet serieus genomen toen hij met data de business problemen en kansen wilde benoemen.

    Dit prikkelde Hamel om, met steun van zijn baas, een MBA te gaan doen. En met resultaat: hij rondde de MBA af behorende tot de top 5 procent van alle studenten. Sindsdien opereert hij op het snijvlak van data en bedrijfsprocessen, ondermeer in het beurswezen en in de verzekeringsbranche.

    Digital is de grote afwezige in het onderwijs

    Hamel’s zeer indrukwekkende loopbaan tonen ondermeer een erkenning als een van ’s werelds weinige Certified Web Analysts, ‘Most Influential Industry Contributor’ door de Digital Analytics Association en mede-beheerder van de grootste community op Google+ over Google Analytics. Toch vindt Hamel zijn allergrootste prestatie het afwerpen van het stempel ‘IT’er’.

    Zijn grootste ambitie voor de nabije toekomst is het schrijven van een tekstboek over digital analytics. Er is veel informatie digitaal beschikbaar, maar er mist nog veel content in offline formaat. Juist omdat ook andere sprekers op #DMD16 wezen naar het achterblijvend niveau van onze HBO- en WO-opleidingen in digitale vaardigheden vroeg ik Hamel welke tips hij heeft voor het Nederlands onderwijs.

    In de basis dient volgens Hamel de component ‘digital’ veel meer als rode draad in het curriculum te worden opgenomen. Studenten dienen daarbij gestimuleerd te worden om de content zelf te verrijken met eigen voorbeelden. Zo komt er in cocreatie tussen docenten, auteurs en studenten steeds betere content tot stand.

    De belofte van big data en marketingautomatisering

    Hamel ziet zeker in B2B de toegevoegde waarde van marketing automation. Je relatie met klant en prospect is immers meer persoonlijk. Marketingautomatisering wordt echter soms foutief ingezet waarbij email wordt ingezet om de indruk te wekken van een persoonlijke, menselijke dialoog. Hamel: “I still believe in genuine, human interaction. There is a limit to how you can leverage marketingautomation.”

    Digital Maturity bron PREZI Joeri Verbossen

    Het grootste probleem bij de succesvolle introductie van marketingautomatisering is dan ook ook de maturiteit van de organisatie. Zolang deze niet voldoende is, zal een softwarepakket altijd vooral een kostenpost zijn. Een cultuuromslag moet plaatsvinden zodat de organisatie de software als noodzakelijke randvoorwaarde beschouwt voor het kunnen uitvoeren van de strategie.

    Dezelfde nuchtere woorden gebruikt Hamel over de belofte van big data. Al te vaak hoort hij in bedrijven: “We need Big Data!” Zijn antwoord is dan: “No, you don’t big data, you need solutions. As long as it does the job, I’m happy.”

    Source: Marketingfacts

  • Making Content Marketing Work

    Making Content Marketing Work

    Speeds & feeds. “Hero” shots. Print ads. Product placement. Really expensive TV advertisements featuring celebrity endorsements.

    Pitching a product and service back when those phrases dominated marketing and advertising discussions seems very quaint today.

    In an era where the incumbent media companies are seeing their audiences fragment across a host of different devices and online sites (including the online versions of the incumbent media providers), those old school techniques are losing their juice.

    Consumers no longer want a spec sheet or product description that tells them what the product or service is — they want to be shown what the product or service can do for them. And they want to see how other actual people — just like them — use the product or service.

    As if that wasn’t tough enough, today’s consumers can spot inauthentic pitches from a mile away. They will happily share your lack of authenticity with millions of their closest friends on Facebook, via Twitter etc., etc. and etc.

    Content marketing has emerged in the past three years as a practice that allows marketers to maintain the balance between richer, deeper information, or content, about their products and doing it authentically.

    Like so many things in life, describing what content marketing is, and what it can accomplish, is way easier than actually doing content marketing successfully.

    In one of Gartner’s earlier docs on content marketing, my colleague Jake Sorofman exhorted marketers to “think like publishers.” Sound advice but many marketers find that to be difficult. To-date, while many marketers are getting much better at sourcing and distributing the kind of content elements for their needs, measuring content marketing’s contribution is not easy. But it can be done.

    Using content analytics gives content marketers insight into how their efforts are being received by consumers, providing the kind of objective measures that previous generations of marketers dreamed of having. Jake’s most research round-up on content marketing has some timely examples of companies which have wrestled with the content marketing challenge and are realizing the value of not merely finding, creating and distributing content, they’re also focusing on using all the tools available to amplify their efforts. The story about IKEA’s work in the area is particularly interesting.

    Yep, times have changed and it’s a much more complex field than marketing used to be. Digital, content, social, mobile marketers are jobs titles that didn’t exist 15 years ago, for the most part. The good news is that the tools and techniques those new job titles require are increasingly available.

    By Mike McGuire | April 6, 2015 |

  • SEVEN STEPS TO CREATING A POWERFUL BI (Business Intelligence) PLAN – And Avoiding the Deadly Analysis Paralysis

    {rscomments on}Have you ever suffered from Analysis Paralysis? That lost-in-the-weeds panic you get when you spend so much time overthinking a situation that you’re unable to act on it? A good 

    business intelligence

    BI plan provides you and your company with information that will enable it to identify and respond to manifold interests, priorities and future needs; sharpens your understanding of your company’s current effectiveness and value and enables you to be predictive in your thinking. There are seven steps to creating a functional BI plan:

    Step 1: Current state analysis
    This is an initial, internal assessment of the prevailing processes, technology and people. The primary focus here is to educate yourself about the status of your current intelligence pipeline and its uses and methods, as well as assessing current internal skills and technology.

    Stage 3: Transformation roadmapStage 2: Future state analysis
    What is your vision? If you are in the exploratory stage, ask yourself what you want your future BI environment to look like; what data sources do you need in order to solve important business challenges? How can you most effectively access and consume information?

    This is the bridge between your current and future states. When designing the transformation roadmap, you should take into account information needs of users and how users want to receive and consume the information. This is where you begin to prove the value of a BI plan.

    Stage 4: Framework
    The framework is the supporting structure that brings together the forces that drive operations in your business: people, processes and technology. The framework must provide a collaborative environment that connects your BI, business processes, collaborative applications and any underlying data stores that already exist. Operational planning involves the process of linking strategic goals and objectives to tactical goals and objectives; it is pertinent at this level of your BI plan. It needs to describe milestones, conditions for success and an operational time period.

    Stage 5: Implementation
    The execution of the BI plan must be agile and adaptive so that the project implementations can be organized and managed effectively. Prioritization is the key in implementation. You should work with business units and departments to prioritize the iterations according to their business needs, which will lead to a value-based implementation plan. Remember too that the landscape changes constantly and your ability to change direction rapidly is vital. At this stage ask the users, ‘What key performance metric will drive value to you?’ Their answers will direct how you
    operationalize the BI Plan.

    Stage 6: Adoption
    Absolute value can only be achieved when the BI plan is adopted by everyone in the company and it penetrates into the business’s processes, when it is fully operationalized and implemented within the organization. During the adoption stage, commitment is required from all users and providers. Their engagement is key. If the BI plan is set up correctly, this should be easy, as the empowering intelligence they will be receiving will become a fundamental factor in their decision-making.

    Stage 7: Tracking
    As part of the adoption process, set key performance indicators to measure the usefulness of your BI plan. Key performance indicators (KPIs) provide insight into the critical success factors of the enterprise and help in measuring progress. Your KPIs must be well-defined quantifiable measurements based on pre-established criteria. KPIs should be designed to measure the intelligence performance against the ask—is this information driving value to you (e.g. ‘what I needed to know’ vs. ‘what I did with it’)? KPIs in your BI plan are not performance targets but are a mechanism to assist you in moving the enterprise towards the desired state, and should have you wondering how you ever lived without this intel.

    If you want to change the way a company acts, you have to begin with changing how it thinks – and in no area is that more true than in Business Intelligence. It gives you insights, predictive ability and, most importantly, the map you need to find your way out of the weeds of indecision.

    Source: www2.freshintelligence.com,23 januari 2015

  • The virtue of doubt: succesful analysts generate deliverables that are being acted upon by decision-makers

    When considering how to select, educate, train and professionally develop the market intelligence officer and analyst of the future, the first thing that came to my mind was an aphorism: “begin with the end in mind”. When attempting to answer this essay’s competition question, properly defining the end may well be imperative prior to beginning. My first step towards an answer thus starts with a question: how to define the end in intelligence?


    The desired end state in intelligence for the purpose of answering our question is defined as:

    “Intelligence is to significantly contribute to the successful definition and execution of a company’s strategy”

    For intelligence deliverables to contribute in this way, the first requirement the deliverables need to meet is that they have to be used by decision-makers. The latter are being defined as the principals of the intelligence work that themselves operate outside the intelligence community. When is intelligence work being acted upon? In my experience, when the work is of convincing quality in at least two dimensions. Excellence is required both in content and in persuasiveness of delivery. This allows us to draw an interim conclusion. The analyst of the future needs to be selected, educated, trained and professionally developed both to deliver work of great content as well as of strong persuasiveness towards the relevant decision-makers. So far, this is nothing new. The requirements to deliver great content in a persuasive manner are timeless. The addictive beauty of intelligence work is hidden in a single word: it is about the future. What analyst can meet these requirements in the future? For this, we need to look forward.

    Intelligence permanently updates the company’s future market environment maps

    Prior to diving into what the future may look like, it has a merit to briefly review the role of intelligence. A

    simple equation may be helpful:

    Threat = Competency + Intent + Surprise

    In this equation, threat is analyzed in terms of how a competitor may hurt our company’s interests. Competencies are defined as the competitor’s (in-) tangible assets. Think of number and size of competitor factories or brands they own. The intent is to summarize the objective of the competitor’s leadership. What plans do they really have to grow their business, given the assets they control today and may have access to tomorrow? The term surprise indicates the power of surprise a competitor may use. What competitor surprises could our intelligence analysts imagine and convince our company’s leadership to prepare for, before they materialize? This equation not only holds true for a single competitor but may also be applied to markets, suppliers, countries etc. A market intelligence function concurrently solves multiple threat equations at any one time. I believe this to be the core role of intelligence in corporate strategy definition and execution. Intelligence is to monitor and forecast all threats a company may face by adding up all relevant threat equations. In so doing, intelligence builds a holistic and permanently actualized picture of the market environment. This picture metaphorically serves as a map for decision-makers to real-time navigate in the market environment and where needed both to change course and adjust the sails. In so doing, intelligence is fully integrated in planning cycles like the plan-do-check-act loop. Given this role, a successful analyst not only permanently updates the map but also ensures the corporate ship is navigated based

    on the latest map.

    In summary, I see two key requirements for the analyst of the future: he knows how the relevant market environment looks in the future and he knows how to persuade the future executive to integrate his intelligence deliverables as input in her decision-making.

    The future may be unpredictable but people do not change

    In this section we are moving back to the future. We face two questions. The first question is: how do we anticipate the future market environment to look like? The second question concerns the professional needs of the future decision-maker. These questions are interlinked. Answering the question what the future market environment for a business will look like will by implication lead to attributes that a successful business leader will need. These attributes will thus determine how she is best served her intelligence needs.

    I feel tempted to describe the generic future market environment. Words like rapidly changing, globally connected and volatile come to my mind. These terms, however, may look wise but are neither actionable nor specific enough for the individual company to be useful. Moreover: I am an analyst not a futurologist. Suffice it to say that the successful analyst of the future needs to offer foresight on the future market environment regardless of what future emerges. Similarly, the successful analyst of the future needs to serve and persuade the future decision-maker regardless of who she happens to be. The latter analyst’ task may well be easier than the task to forecast the future market environment. Different market environments may bring different leaders to

    executive positions, but people as such do not fundamentally change. Basic human psychological needs like recognition, association or power are not affected by changing currency exchange rates or shifting economic and political power. For an analyst to become persuasive, grasping the psychology of executive power is imperative, regardless of this morning’s level of the Dow Jones Index.

    In summary, we thus need to educate and train the analyst both for any future market environment that may emerge and for serving any decision-maker. Before starting with an analyst’s education, we clearly first need to select those analysts that naturally are best suited for such future role. Let us now consider some analyst’ selection criteria.

    Market intelligence is a smart people’s business and offers a rewarding career

    In my experience, intelligence is rarely a numbers war. Throwing loads of people at an intelligence puzzle is not by definition conducive to solving it rapidly and elegantly. Reginald V. Jones - head of British Scientific Intelligence during World War II – corroborates my view when he writes how an intelligence team is to be set up [Jones, 1978]:

    “The size of the staffs […] should be kept as numerically small as possible, and that quality was much the most important factor”

    His counterpart in WW II British Naval Intelligence, Admiral John Godfrey even more passionately advocates the relevance of the quality of the analyst [Macintyre, 2010]:

    “It is quite useless, and in fact dangerous to employ people of medium intelligence. Only men with first-class brains should be allowed to touch this stuff. If the right sort of people cannot be found, better keep them out altogether.”

    When selecting first class intelligence officers, what quality do I believe we need to look for? In line with common human resource practices, I distinguish functional and behavioral competences. A critical functional competence in selecting an analyst I believe is intellect. Being smart, however, is not enough. When selecting an analyst, I also look for research skills, knowledge of business administration, language skills and increasingly for consultancy skills. An analyst that has no feel how to connect to a decision-maker may be highly useful in a team, but the smaller the team, the more every team member has to able to act independently. An analyst needs to be thinking on her feet, i.e. being always prepared, even when she unexpectedly happens to share the elevator with their decision-maker customer. In job interviews I ask candidate analysts their elevator pitch. The candidate better has to score an instant sell to stay in the recruitment process.

    In behavioral competences, I think of qualities like curiosity, frustration tolerance, persuasiveness, humility, perseverance, courage, discreetness and innovativeness. There are two more distinctive qualities in a candidate I look for. The first is the degree of xenocentric thinking that the candidate demonstrates. In analysis, putting the other party central is critical when aiming to understand their current steps and predicting their next steps.

    The final required quality is the candidate’s passion for intelligence work – embracing the puzzle game as a mean to have a rewarding career. I intentionally write career, not job. Intelligence is one of these crafts where experience matters more than proportionally. In inte

    lligence the 10,000 hour rule certainly applies: no mastery without extensive practice. As long as artificial intelligence cannot replace analysts, we need to select analysts that are willing to dedicate their lives to the cause. In return an analyst should not only be entitled to an intellectually rewarding profession but also to

    a decent financial package. The best employers have developed a dual career ladder, where becoming a thought leader is equally conducive to receiving higher job grades as becoming a leader of ever larger numbers of staff. There is a catch though. I try to avoid selecting Mammon’s mercenaries. If your pay matters more than your puzzle, try Wall Street.

    Educate and train to doubt, not to know

    There is a critical difference between chess and cheese. In chess, rules are fixed and adversaries change. In the cheese business, rules – paradigms if you wish – are fluid and adversaries tend to remain the same. Even when today’s paradigm is that cheese is mainly sold through supermarkets, tomorrow’s rule may be that it is sold through e-commerce. As a cheese producer, you are often still up against the same competitors, only the channel has changed. Why does this matter to how to educate

    If cheese would be like chess, the focus in analyst education would be on a one-off learning of business’ paradigms and related functional tools. Provided we have selected the best and brightest minds as analysts, education would only require a relatively modest effort. Listening skills and reasonably regular class attendance would already take our analyst a long way.

    Tools and traits to learn in intelligence include classics like the intelligence project cycle. The cycle consists of brief reception, project definition, collection, analysis, reporting and filing and finally customer debrief. For each of the phases of the cycle, both functional and behavioral analyst skills need to be educated. Think of project management skills, of OSINT- and/or HUMINT-collection courses, of courses in analysis tools, in the psychology of bias and in strategy, of a course in slide writing and story-lining, of a course in consultancy skills including personal effectiveness and last but not least of a mandatory training in ethics and applicable compliance. The above list is not exhaustive and should be tailored to the job needs of the future analyst rather than the other way around.

    The real effort to educate and train a future analyst would have to be spent on training by doing, with the analyst in training over time independently taking on ever more complex problems. In so doing, the analyst in training gradually builds a mental library of multidimensional intelligence problems and related solutions. In a sharp mind, problems and their solutions are unconsciously filed as patterns. The 10,000+ hour experienced analyst has an instant grasp of a complex new problem, because he intuitively recognizes the new problem as matching a mentally stored pattern. Moreover, the pattern informs him on the script that is likely to enroll for this type of problem and thus the most appropriate course of action to take. Problem solving through pattern recognition built upon experience indeed is a source of power in chess [Klein, 1999].

    Chess, however, is not cheese. The fundamental difference may be summarized in a single word: doubt. The cheese analyst for sure needs to take the education and training summarized above. It is imperative that all business strategy tools need to be mastered, next to intelligence-specific skills. The 10,000 hour rule certainly applies as experience makes a proven difference [Mandel, 2014]. That is all similar and true for chess and cheese.

    There is a difference though. In contrast to the expert fire fighters and the master chess players discussed by Klein, the cheese business as we know it has neither Laws of Physics nor fixed rules as its basis. The most fundamental asset that education and training may deliver a future analyst in the cheese business9 is that single intelligence virtue: doubt.

    This is where foxes and hedgehogs enter. The ancient Greek poet Archilochus has written a line that has inspired today’s forecasting professionals such as Silver and Tetlock [Silver, 2013]:

    “The fox knows many little things, but the hedgehog knows one big thing”

    Silver uses this line to distinguish between poor and above-average forecasters. We already discussed above that the analyst of the future should be selected, educated and trained to be successful in predicting market or competitor future moves in any future, serving any future decision-maker.

    It seems a limited stretch to apply the apparent critical behavioral skills of an above-average forecaster to a successful future intelligence analyst. Silver coins his poor forecasters hedgehogs:

    “Hedgehogs […] believe in Big Ideas – in governing principles about the world that behave as though they were physical laws and undergird virtually every interaction in society”

    In cheese intelligence, a Big Idea could be that cheese is yellow – with the analyst conveniently overlooking that white mozzarella cheese is amongst the fastest growing cheese types. Another Big Idea could be to only look for competitors in traditional sales channels.

    The above-average forecasters are positioned as foxes [Silver, 2013]:

    “Foxes […] believe in a plethora of little ideas and in taking a multitude of approaches toward a problem. They tend to be […] tolerant of nuance, uncertainty, complexity and dissenting opinion.”

    The fox embraces her multitude of small sources and views. Doing so calms her fundamental doubt about how fast paradigms in her business over time shift. She realizes that such shifts may render her library of historically obtained patterns dangerously obsolete and by implication render her script-related historically successful courses of actions no longer adequate. That is good thinking! It raises a question though. How to educate and train a smart fox?

    The question is whether a smart fox may result from education and training in the first place. Behavioral economist and Nobel Prize laureate Daniel Kahneman is skeptical of the role of education in topics such as this where psychology seems to matter [Kahneman, 2011]:

    “Teaching psychology is mostly a waste of time”

    Still, the final result of our efforts to select, educate, train and develop analysts remains unchanged. The future analysts need to deliver intelligence that is to significantly contribute to the successful definition and execution of a company’s strategy. We have identified that proper selection, education and training may to a large degree contribute to build that very analyst. We cannot be sure yet, whether these efforts have seeded enough doubt in the analyst’s mind to embrace foxiness. The latter thus is our key attention point in us developing our analysts.

    Developing analysts’ self-confidence makes them radiate and receive trust

    We have so far defined the role of intelligence. Intelligence outputs seamlessly need to integrate into decision-making processes. We subsequently reviewed the selection and curriculum of our future analysts. What is left to do? We now still need to ensure that things move beyond thinking and into execution and to ensure a well-trained analyst remains foxy. It is the intelligence educator’s and practitioner’s responsibility to ensure both that the intelligence output from all analysts – junior and senior - is indeed being fruitfully used by the corporate decision-making for whom it is intended – and that the analysts become and remain foxy.

    Both educators and senior intelligence practitioners should develop the analysts of the future to make the ultimate connection: building trust with the company’s management as a catalyst and safe-guard to ensure our intelligence deliverables are considered critical input in their decision-making. We need to repeat it: intelligence is a people’s business. A decision-maker first needs to trust the analyst prior to trusting the analyst’s work. I have seen foxiness contribute to credibility. More than once I witnessed management being relieved that intelligence took a profoundly non-dogmatic, non-partisan position, highlighting pros and cons and taking multiple perspectives. Objectivity is a rare currency in top management echelons. Harvest as intelligence staff its value and coach future analysts to do the same.

    For a foxy analyst to receive trust it is critical that she first trusts herself. Senior managers tend to be equipped with a remarkable sense for discovering their advisors uncertainty. Even when they may not say it, they may still - perhaps even unconsciously - think: if you don’t trust yourself, how can I trust your judgement on this intelligence topic?

    Moreover, uncertainty is hard to mask. It reveals itself in subconscious body language, in tone-of-voice and in the very phrases used to present an intelligence deliverable. To prevent personal uncertainty to reduce or annihilate the impact of what factually is a good intelligence brief, developing an analyst requires to develop his self-confidence; his trust in his own judgement. This requires on-the-job coaching, praising every piece of good work and constructively suggesting improvements to less-than-perfect deliverables.

    As a food company, we at FrieslandCampina see building self-confidence in our future leaders as part of a broader human resource initiative called “nourishing leadership”. First we aim to select well-educated talent. Once selected, we offer a challenging and inspiring on-the-job and formal training and development setting. In our company senior leaders are held accountable for creating a climate in which our talents for the future thrive, regardless of whether their career path leads them to people leadership or to thought leadership. Self-confidence builds with exposure. So we let the analysts deliver their good work to higher management by themselves as soon as they are up to it. When they know they get the chance to practice senior management advisory whilst their boss will back them up and support them in the meeting where needed, they also build flying hours in executive consulting. Success and experience breeds more self-confidence. This usually leads to a virtuous development cycle.

    To nurture the best connections between analyst and managements I believe starts with managing expectations at both interfaces: that of the (junior) analyst and that of senior management. Senior management needs to understand that good intelligence work can reduce but not remove uncertainty. Analysts needs to accept that when their intelligence output and possibly their recommendations for courses of actions are ignored by line managers it doesn’t mean their work was of poor quality or even worse that rejection reflects distrust in their capabilities.

    In my experience, inevitable and occasional rejection of work is easier to accept when the larger perspective is kept in mind. No matter how wonderful intelligence is as a profession, the role of intelligence in the greater company should not be exaggerated, not even by its most passionate practitioners. Intelligence analysts are only those employed to analyze and predict the business environment with a truly open and doubtful mind, bringing home the key messages to senior decision-makers with a keen and emphatic view of their customer’s agendas and feasible management choices.

    Executives anyway very well know that at all times the buck stops, when it stops, at their management table. Today’s and tomorrow’s best intelligence analysts, however, ensure that very table does not stand in their company’s offices.


    Author: Erik Elgersma

    Source: Linkedin posts

  • What is the critical competitive intelligence skill?

    Asking the right question.



    “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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