4 items tagged "actionable insights"

  • How to use data science to get the most useful insights out of your data

    How to use data science to get the most useful insights out of your data

    Big data has been touted as the answer to many of the questions and problems businesses have encountered for years. Granular touch-points should simplify making predictions, solving problems, and anticipating the big picture down the road. The theory behind data science is a law of large numbers; similar to quantum physics, when we try to predict or analyze data lakes to draw a conclusion, it can only be a probability. Data cannot simply be read, it’s like a code that needs to be cracked.

    There’s an incredible amount of insight that can be gleaned from this type of information, including using consumer data to better inform their strategies and bottom lines. But the number of businesses that are actually implementing actionable steps from their data is minimal. So, how can companies ensure that they’re effectively managing the data they’re collecting in order to improve business practices?

    Identify what you’re looking to learn

    Too many companies invest heavily in software and people in a quest for big data and analytics without truly defining the problems that they’re looking to solve. Business leaders expect to instantly throw a wide net over all datasets, but they won’t necessarily get something useful in return.

    Take, for example, a doctor that spent over a year and a half implementing a new system that was supposed to give his colleagues meaningful medical insights.

    After collecting the data without truly defining the problem they wanted to solve, they ended up with the following insight: “Those who have had cancer have had a cancer test.” This, obviously, is a true statement culled from the data. The problem is it’s useless information.

    The theory behind data science was never meant for small data sets, and scaling to do so comes with a host of issues and irregularities; however, more data doesn’t necessarily mean better insights. Knowing what questions to ask is as important for a company as having the best tools for thorough data analysis.

    Prepare your data to be functional

    They say practice makes perfect, but with data science, practice makes permanent if you’re doing it the wrong way.

    The systems that companies use to keep track of data don’t have a lot of validation. Once you start diving into big data for insights, you realize there’s a whole layer of “sanitization” and transformation that needs to happen before you can start running reports and gleaning useful information.

    We’ve seen major companies doing data migration, but with an accuracy rate of 53%. Imagine if you went to the doctor mentioned in the previous section and he admitted his recommendations were only 53% correct. We can make a big bet you’re not going to that doctor anymore.

    To get quality data, you have to understand what quality data looks like. The human element and the machine have to work together; there needs to be an actionable balance. Data sources are constantly in flux, grabbing from new inputs from the outside world, ensuring a useful level of quality on the data coming in is critical or you’ll get questionable results.

    Depend on a reliable tech solution

    Once you have a clear path of checks and balances to ensure you’re on the right track, establishing a minimum viable product — potentially with a more efficient outsourced team — is what will truly drive actionable results. It makes sure the assumptions and projections derived from the insights are continually up to date, and looks from different angles to anticipate major trend changes.

    It’s important to see the big picture, but also be able to change a model’s behavior if it’s not delivering the most valuable insights. Whatever solution you settle on might not necessarily be the most sophisticated, but as long as it’s providing the answers to the right questions, it will be more impactful than something complex and obscure.

    When companies employ tools to untangle their stores of data without having a deep understanding of the limitations of data science, they risk making decisions based on faulty predictions, resulting in detriment to their organization. That means higher costs, incorrect success metrics and errors across marketing initiatives.

    Data science is still evolving very quickly. Although we will never get to the point that we can predict everything accurately, we will get a better understanding of problems to provide even more useful insights from data.

    Author: Luming Wang

    Source: Insidebigdata

  • The struggle of B2B companies to find customized Market Intelligence

    The struggle of B2B companies to find customized Market Intelligence  

    As a company operating in a B2C environment, life is easy. More explicitly, acquiring the right market information is a rather direct process. Countless reports filled with rich consumer insights are available at your fingertips. These reports, which cover topics like market size, consumer profiles, competitors, and trends, are easily accessible through sales and marketing professionals. With the right approach, available information can also be directly translated into clear insights on a strategic level. In the boardroom, market intelligence serves as a reliable sparring partner, setting the direction for strategic actions.

    Unfortunately, the opposite is the case for B2B companies. Their markets can often feel like a massive black box filled with blind spots. Also, the majority of leading market research companies focus on producing market reports for B2C companies, because the required data is significantly more convenient to obtain and more widely available. Besides, B2C companies are more willing to invest in market intelligence reports, due to the better overall quality of the data and insights.

    However, possessing the right intelligence is also vital for B2B players, especially in the fast changing and dynamic business environment they are operating in. Having access to information about market size, competitors, and industry trends can make the difference between staying on top of your league or to be disrupted.

    Existing market reports for B2B companies are difficult to put to direct action, as they are extremely standardized and frequently based on extrapolations of historical figures. Aside from the inaccuracies, these reports, in general, only provide you with insights about the past, whereas trustworthy market intelligence also helps you to be proactive instead of reactive, with respect to the near future.

    Another issue with these reports is the phenomena of 'information overload'. Decision makers drown in huge research reports filled with endless pie charts and tables. By the time they reach page 299, any actionable insight is definitely lost, and the reader is left behind frustrated.

    Sounds familiar?

    Luckily, there are several methods through which professionals in a B2B environment can start creating their own customized market intelligence.

    Today’s world offers one enormous advantage: the availability of rich and infinite open-source intelligence (OSINT). Endless bits and pieces of information are available on the open web; hidden in databases, social media content, trade journals and news articles. Connecting all the pieces of the puzzle in a smart way leads to better understanding of your market.

    Another method of creating tailor-made market intelligence is through (predictive) modelling. Key factor is defining which variables affect the topic you want to clarify. Take for example market sizing. Some variables might be less obvious than others. Illustrative for B2B companies is that they often act as a shackle in the middle of a value chain. Also, B2B products and their applications are more multifaceted compared with their B2C counterparts. It can be necessary to count back from end volumes of a product and combine this with market characteristics to estimate the market size of a specific commodity.

    The illustrations mentioned above are just two plain examples of techniques that can be valuable. Obviously, many more methods and tools are available. The trick is finding the right combination of methods and tools. As well as in depth understanding of how to determine validity.

    However, the bottom line remains unchanged: by combining outcomes of different techniques proper market intelligence can be gathered, even in a B2B environment. Aside, it is important to periodically update your data and insights with new figures and trends. Check and double check your data model with industry experts and internal sources. By doing this

    By building market intelligence in a systematic (and continuous) way, insight in your market keeps increasing, and the black B2B box can be whitened step-by-step.

    Author: Egbert Philips

    Source: Hammer Market Intelligence

  • The transformation of raw data into actionable insights in 5 steps

    The transformation of raw data into actionable insights in 5 steps

    We live in a world of data: there’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Dealing with Data is your window into the ways organizations tackle the challenges of this new world to help their companies and their customers thrive.

    In a world of proliferating data, every company is becoming a data company. The route to future success is increasingly dependent on effectively gathering, managing, and analyzing your data to reveal insights that you’ll use to make smarter decisions. Doing this will require rethinking how you handle data, learn from it, and how data fits in your digital transformation.

    Simplifying digital transformation

    The growing amount and increasingly varied sources of data that every organization generates make digital transformation a daunting prospect. But it doesn’t need to be. At Sisense, we’re dedicated to making this complex task simple, putting power in the hands of the builders of business data and strategy, and providing insights for everyone. The launch of the Google Sheets analytics template illustrates this.

    Understanding how data becomes insights

    A big barrier to analytics success has been that typically only experts in the data field (data engineers, scientists, analysts and developers) understood this complex topic. As access to and use of data has now expanded to business team members and others, it’s more important than ever that everyone can appreciate what happens to data as it goes through the BI and analytics process. 

    Your definitive guide to data and analytics processes

    The following guide shows how raw data becomes actionable insights in 5 steps. It will navigate you through every consideration you might need to make about what BI and analytics capabilities you need, and every step of the way that leads to potentially game-changing decisions for you and your company.

    1. Generating and storing data in its raw state

    Every organization generates and gathers data, both internally and from external sources. The data takes many formats and covers all areas of the organization’s business (sales, marketing, payroll, production, logistics, etc.) External data sources include partners, customers, potential leads, etc. 

    Traditionally all this data was stored on-premises, in servers, using databases that many of us will be familiar with, such as SAP, Microsoft Excel, Oracle, Microsoft SQL Server, IBM DB2, PostgreSQL, MySQL, Teradata.

    However, cloud computing has grown rapidly because it offers more flexible, agile, and cost-effective storage solutions. The trend has been towards using cloud-based applications and tools for different functions, such as Salesforce for sales, Marketo for marketing automation, and large-scale data storage like AWS or data lakes such as Amazon S3, Hadoop and Microsoft Azure.

    An effective, modern BI and analytics platform must be capable of working with all of these means of storing and generating data.

    2. Extract, Transform, and Load: Prepare data, create staging environment and transform data, ready for analytics

    For data to be properly accessed and analyzed, it must be taken from raw storage databases and in some cases transformed. In all cases the data will eventually be loaded into a different place, so it can be managed, and organized, using a package such as Sisense for Cloud Data Teams. Using data pipelines and data integration between data storage tools, engineers perform ETL (Extract, transform and load). They extract the data from its sources, transform it into a uniform format that enables it all to be integrated. Then they load it into the repository they have prepared for their databases.

    In the age of the Cloud, the most effective repositories are cloud-based storage solutions likeAmazon RedShift,Google BigQuery, Snowflake, Amazon S3, Hadoop, Microsoft Azure. These huge, powerful repositories have the flexibility to scale storage capabilities on demand with no need for extra hardware, making them more agile and cost-effective, as well as less labor-intensive than on-premises solutions. They hold structured data from relational databases (rows and columns), semi-structured data (CSV, logs, XML, JSON), unstructured data (emails, documents, PDFs), and binary data (images, audio, video).  Sisense provides instant access to your cloud data warehouses.

    3. Data modeling: Create relationships between data. Connect tables

    Once the data is stored, data engineers can pull from the data warehouse or data lake to create tables and objects that are organized in more easily accessible and usable ways. They create relationships between data and connect tables, modeling data in a way that sets relationships, which will later be translated into query paths for joins, when a dashboard designer initiates a query in the front end. Then, users, in this case, BI and business analysts, can examine it, create relationships between data, connect and compare different tables and develop analytics from the data.

    The combination of a powerful storage repository and a powerful BI and analytics platform enables such analysts to transform live Big Data from cloud data warehouses into interactive dashboards in minutes. They use an array of tools to help achieve this.Dimension tables include information that can be sliced and diced as required for customer analysis ( date, location, name, etc.). Fact tables include transactional information, which we aggregate. TheSisense ElastiCube enables analysts to mashup any data from anywhere. The result: highly effective data modeling that maps out all the different places that a software or application stores information, and works out how these sources of data will fit together, flow into one another and interact.

    After this, the process follows one of two paths:

    4. Building dashboards and widgets

    Now,developers pick up the baton and they create dashboards so that business users can easily visualize data and discover insights specific to their needs. They also build actionable analytics apps, thereby integrating data insights into workflows bytaking data-driven actions through analytic apps. And they define exploration layers, using an enhanced gallery of relationships between widgets.

    Advanced tools that help deliver insights include universal knowledge graphs and augmented analytics that use machine learning (ML)/artificial intelligence (AI) techniques to automate data preparation, insight discovery, and sharing. These drive automatic recommendations arising from data analysis and predictive analytics respectively. Natural language querying puts the power of analytics in the hands of even untechnical users by enabling them to ask questions of their datasets without needing code, and to tailor visualizations to their own needs.

    5. Embed analytics into customers’ products and services

    Extending analytics capabilities even further, developers can create applications that they embed directly into customers’ products and services, so that they become instantly actionable. This means that at the end of the BI and analytics process, when you have extracted insights, you can immediately apply what you’ve learned in real time at the point of insight, without needing to leave your analytics platform and use alternative tools. As a result, you can create value for your clients by enabling data-driven decision-making and self-service analysis. 

    With a package like Sisense for Product Teams, product teams can build and scale custom actionable analytic apps and seamlessly integrate them into other applications, opening up new revenue streams and providing a powerful competitive advantage.

    Author: Adam Murray

    Source: Sisense

  • Toucan Toco breidt team in Amsterdam uit met Tim Bosman en Elisabet Queralto Garzon

    Toucan Toco breidt team in Amsterdam uit met Tim Bosman en Elisabet Queralto Garzon

    Toucan Toco, specialist in data storytelling, breidt het team in Amsterdam uit met Tim Bosman en Elisabet Queralto Garzon. Bosman richt zich in zijn nieuwe rol voor Toucan Toco op Business Development. Queralto Garzon vervult de positie van Project Manager. Met de uitbreiding van het team kan Toucan Toco concreet invulling geven aan het verwezenlijken van de groei-ambities op de Nederlandse markt.

    Met data storytelling wil Toucan Toco niet alleen managers, maar ook medewerkers voorzien van inzichten waarmee zij gefundeerde beslissingen kunnen nemen. Het van origine Franse bedrijf opende ruim een jaar geleden een eigen kantoor in Amsterdam. Onlangs werd Yann Toutant aangesteld als Country Manager voor het neerzetten van een stevige structuur waarmee het bedrijf een goede uitgangspositie heeft om sterke groei te realiseren. De uitbreiding van het team met Tim Bosman en Elisabet Queralto Garzon is hiervoor een eerste stap.

    Tim Bosman is een ervaren commercieel manager met een achtergrond in finance, vastgoed en business management. Met zijn kennis van zowel verkoop als management en een sterke focus op bedrijfsbeheer voegt hij waardevolle expertise toe aan het Nederlandse team. “Om succesvol te kunnen zijn zou data breed toegankelijk moeten zijn en niet alleen voorbehouden blijven aan een kleine groep mensen”, zegt Tim Bosman, business development manager bij Toucan Toco. “Ik vind het een mooie uitdaging om de expertise en toegevoegde waarde van Toucan Toco naar Nederlandse organisaties te brengen zodat iedereen kan profiteren van sterke inzichten en op basis daarvan daar betere beslissingen kan nemen.”

    Elisabet Queralto Garzon heeft de nodige ervaring opgedaan als project manager op zowel de internationale als de Nederlandse markt, onder meer bij Effectory. Daar begeleidde ze onder andere een project bij Ikea, waarbij ze verantwoordelijk was voor de succesvolle implementatie van HR software in meer dan 50 landen. Ze zal deze ervaring nu inzetten om de Nederlandse klanten van Toucan Toco te ondersteunen. 

    “De oplossing van Toucan Toco is ontzettend slim en tegelijkertijd zo eenvoudig als het gebruiken van een app op je smartphone”, zegt Elisabet Queralto Garzon, project manager en customer success manager bij Toucan Toco. “Ik zie er naar uit om klanten te helpen het volledige potentieel van de oplossing optimaal te gaan gebruiken.” 

    12 miljoen euro groeikapitaal 

    Toucan Toco is opgericht in 2014 heeft inmiddels kantoren in de Benelux, Frankrijk, Spanje, Italië en Amerika. Onder de ruim 100 klanten die gebruikmaken van de oplossingen van Toucan Toco bevinden zich onder meer Engie, Heineken, Sodexo en Renault Nissan. In november 2019 ontving het bedrijf in een eerste investeringsronde 12 miljoen euro groeikapitaal. Met het geld wil Toucan Toco onder meer de aanwezigheid in de Benelux-markt uitbreiden en de ambitie voor marktleiderschap verwezenlijken.

    Auteur: Yann Toutant 

    Bron: Toucan Toco

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