Hackernoon logoHow To Build a Data-Savvy Brand by@viceasytiger

How To Build a Data-Savvy Brand

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@viceasytigerVik Bogdanov

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Since new-gen tech has enabled companies to mine large sets of structured and unstructured data, the idea of becoming a data-driven company has become the preoccupation of many executives.
Predicting new market trends, enhancing security, understanding customer behavior – in a data-driven company, big data analytics eliminate the guesswork and allow the executive teams to take calculated risks when it comes to making business-critical decisions.

No company becomes data-driven overnight.

Technological innovations, infrastructural changes, but, most importantly, major mindset shifts on all organizational levels are essential to instill a data-driven culture within an organization. 
Let's review what it takes to build a data-savvy brand and how to instill data analytics and/or data science in your company's DNA to better meet your business goals.

What is a data-driven culture?

Generally speaking, data-driven cultures are cultures where all the key players unanimously agree to rely on data when making business-critical decisions. Such cultures may be inherently different, but they do share the same essential features:
1) Being ready to invest in working with data: capturing data, storing, processing, and analyzing it requires complex data analytic tools that run on top of advanced infrastructure. A data-driven culture embraces digital transformation and is ready to invest time and money into working with data.
2) Being ready to listen to data: in a data-driven culture, people are ready to listen to data before making important decisions. They are ready to align their decisions to insights derived via data mining, as opposed to cliches, familiar patterns, and emotionally charged opinions.
3) Being capable of reading data: mining data for insights requires analytical skills. Even though most of today’s big data apps will present data in understandable and readable formats, core decision-makers should be able to read these graphs and diagrams and interpret them correctly. Misinterpretations may cost your business dearly – the reputation and revenue are at stake if your decisions take the wrong turn.
4) Being able to trust data: as much as data-driven cultures rely on data, they do not rely on it blindly. If a manager says s/he would rather rely on their firsthand experience instead of analytics reports, they're not necessarily wrong. What if the analysts have overlooked one or several vital factors? Data analysis doesn’t put human experience off counts: it's capable of engaging in constructive dialogue that matters.
Understandably, instilling a data-driven culture comes naturally, when the executive team believes firmly in its advantages and is ready to steer the entire company towards becoming data-driven. For an organization genuinely dedicated to becoming data-driven, it all begins with creating a data infrastructure.

Four Steps To Creating A Robust Data Infrastructure Within Organization

Apart from adopting a specific mindset, what are the practical steps to creating a data infrastructure? Below is a step-by-step plan you could potentially adapt to your unique situation. 
Step 1: Retrieving existing data
The first issue you are likely to come across is the lack of data. Rest assured, though, your back office infrastructure already has loads of it stored in disparate databases and CRMs. Even before you start gathering metrics using advanced tools and sensors, you already have something you can start with. Retrieving this data may pose a real challenge: those old databases were built without considering that someone would want to extract data for analysis.
Step 2: Identifying the data you need
Having extracted the existing data, you are ready to categorize it and identify the data gaps you need to fill by collecting new metrics. The tricky part about capturing data is only collecting the data you need. One more thing you should take into account if you’re working with data, is compliance with privacy regulations such as, for example, GDPR in the EU. All data you collect for analysis should only serve your business purposes.
Step 3. Decide on optimal data analytics toolset
Now that you have retrieved existing data, and identified the full range of metrics, you will analyze to drive your business forward, decide on an optimal data analytics toolset. Depending on your organization type, the tools may differ: from simple and easily accessible ones like Google Analytics to complex enterprise-grade systems like Splunk, Hadoop, or Cloudera. For a medium-sized company, tools like Google Reporting API and HotJar may be enough for frontend analytics and basic marketing needs. Today, data analytics vendors offer a wide range of tools for data mining, engineering, and warehousing.
Step 4. Allocate IT infrastructure capacities for storing and processing data
The data you collect will place high demands on an underlying IT infrastructure. You need to identify where you will store data; also, if you plan to run sophisticated enterprise-grade big data apps, they will require computing capacities for data processing and analysis. You may have to upgrade your existing infrastructure if you want to keep all of your data operations in-house or rent cloud capacities for your data analytics projects.

Being Data-Savvy: Issues and Challenges

Once your company becomes data-savvy, you are sure to come across several issues. One of them is a lack of buy-in from top management and employees. Too often, companies that strive to be data-driven find it hard to explain why a particular metric is important to people on their teams. People usually start to recognize the mertic’s value as soon as they comprehend its purpose and its direct impact on revenue streams. Sooner or later, most people get accustomed to new ways of working and start treating metrics with the respect they rightfully deserve. 
Other difficulties involve:
  • Retrieving particular metrics;
  • Ensuring the metrics are set correctly;
  • Deciding on a report type - it should be easy to read and comprehend;
  • Determining if a certain parameter has a statistic value;
  • Checking the collected metrics: by collecting more data, or by gathering more different metrics from other sources.
Most people find it hard to tackle these difficulties on a routine basis. Obviously, this requires training, expertise, and talent. Data-driven companies usually hire strong data scientists and data analysts or create full-fledged analytic departments for processing large volumes of data they collect from a range of metrics. 
Data pools, data lakes, and data warehouses
Other growing pains you are likely to experience on your way to becoming a data-savvy business stem from the fact that the data you collect often resides in disparate databases and repositories. Data scientists and analysts usually know how to work with some of them (like, for example, SQL), and are completely unfamiliar with the rest. Comparing the data from these databases will usually pose a particular difficulty. 
For example, you may want to compare the data on website users browsing behavior with their customer activity. This will require synchronizing data from Google Analytics with CRM data. Usually, Data Warehouse Systems (DWS) will offer solutions to this problem by collecting data from different pools into a more extensive data lake. 
At this stage, most companies recognize the importance of hiring data engineers to work on data infrastructure and DWS development. Hiring qualified experts, though, is yet another challenge. 

Hiring the right people

As said above, it takes time to instill a data-driven mindset among existing employees. It rarely happens without a consistent effort of the management team to communicate the value of the data-driven business. Coaching talented insiders to work with data is one of the paths a company may take to develop internal expertise. 
Some companies choose to go the hard way and hire pre-vetted and seasoned data analysts. Unfortunately, such experts are in high demand and often command big salaries. An average data scientist salary in the USA is $120,788 per year, according to Indeed. However, if the budget allows, and if the company is keen on retaining expertise in-house, this is one of the possible solutions to talent shortage issues.
Firms looking for a combination of affordability and expertise often choose to hire strong data scientists in popular outsourcing locations like Ukraine or India that have abundant yet untapped data science talent pools and provide cost arbitrage.

Improving predictability with data analytics

Analytic software generates reports; however, data analytics are inherently different from business reporting. Reporting is usually related to past activity, while data analytics are potentially capable of predicting future outcomes. Not only do predictive analytics forecast market trends and predict customer behavior, but they are also capable of estimating how much revenue a particular new product feature will generate. Ideally, advanced business intelligence is your next destination on the path towards becoming data-driven, adopting more targeted strategies, and making informed decisions.
As challenging as the becoming data-driven may look, paced approach to implementing change is arguably the most effective. To truly deliver business value, your data infrastructure should grow gradually in synch with your company development. One of the prerequisites to becoming a data-driven company is a dedication to constant improvement and readiness to undergo endless cycles of tests and experiments. At the end of the day, the rewards you reap will match your efforts and investments. 

What's your experience with building data-driven brands and/or products?


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