A Few Important Elements of a Successful Big Data Analytics Strategy

 

While nearly all the companies know that their data is a strategic asset, most of them are not taking full benefit of big data analytics to get ahead. This post explains the key elements of a successful data strategy which will assist you make decisions on the basis of right data analysis instead of hunches.

Companies know that their product data is a strategic asset and plan to use it to make better decisions, but the issue is that it is complicated. Usually, the data gets distributed in silos, caught in departmental systems which don’t work well with each other, the data quality is poor and costs related to it are really high. Besides handling the market pressures, most companies will prioritize the critical, strategical and routine requirements over the long-term strategic initiatives.

Heading towards a totally data-driven culture is definitely possible and it begins with a powerful and effective big data strategy, which usually seen as a technical practice, but a modern and broad data strategy addresses more than just data and is a roadmap which defines the people, process and technology for proper big data analytics.

An effective data strategy must outline a detailed plan to evolve in analytic abilities and evolution from making decisions on the basis of observation to make decisions with prudence.

Let’s know now how you can get started with your own effective data strategy.

Take a Look at a Few Important Elements of a Successful Big Data Analytics Strategy:

 

1.  Business Needs: Data has to address particular business needs for meeting the company’s strategic objectives and generate actual value out of it. The first step to set the business needs is to recognize a winner, all stakeholders and SMEs in the company. The winner of the data strategy is the executive leader that will gather the support for the investment. Stakeholders and SMEs will represent certain departments or functions in the company. After that companies must set the strategic objectives and integrate department activities to company objectives. It is obvious to have company objectives and department level objectives, but the stated objectives for both levels must be synchronized. These goals are most efficiently collected through an interview process which begins at the executive level and then continues down towards the department leaders. With this process, you can find out what the leaders are trying to asses and improve, questions they need answers for and then the KPIs to answer those questions. Through beginning with collecting and documenting the business needs, companies overcome the first obstruction to many IT or technical projects- knowledge of what the business is trying to accomplish.

 

2.  Sourcing and Collecting the Data: With proper understanding of what questions exactly the business is asking, companies can shift to the next element- analyzing data sources, in what ways that data is collected and where exactly the data really exists. It is doubtful that all company data is available in the company and that it already available in a place which is accessible. So, companies should work backwards to find out the source. For data which can be found in-house, your big data analytics team must note that the source system and any hurdles to getting access to this data. Make sure that you find out whether the data has the correct level of detail and is updated with the correct frequency to reply back to the question successfully.

3.  Technology Infrastructure Needs: Never get over-involved in the buildup and the latest technologies, rather concentrate on the commercial reasons for your initiatives. Establishing a flexible data architecture is a complex subject for which there are several options and strategies, so below mentioned are a few vital things to consider:-

• To what degree can an existing operational system support big data analyticsrequirements? Well, very little. It is usually not the most excellent approach to depend on an operational system to fulfill analytical requirements that means a central data repository can be very useful.

 

• Whether your company has the ability and technical infrastructure to support a data warehouse on-premise or would it leverage a big data in cloud solution, think what makes more sense.

 

• Find out whether there is a standard integration tool to access the data through the source systems in the central repository or not. Will this architecture layer get leveraged for commercial logic, so the data is all set to be used?

All of the above mentioned considerations will get within an overall architecture and as with nearly all designs, the more your needs and future requirements are considered, the more the big data analytics solution will really support the business.

 

4.   Transform the Data into Insights: A data strategy must offer suggestions for how to apply big data analytics to dig out business-critical insights, and big data visualization is the key! A lot of new-age companies still depend on Excel, e-mail or an inheritant business intelligence tool that do not allow to communicate with the data. Often a boring and manual process is needed and depending on IT to build reports makes a blockage. Data visualization tools must make the data look excellent and make the data simpler to understand.

 

5.   Processes And People Involved: In this step companies monitor the people within their company and the processes connected with building, sharing and ruling data. A data strategy will possibly introduce more data and big data analysis and even new tools. On the basis of this, it looks sensible enough to look at the talents of the users to comprehend their strengths and where exactly they might need support. Employee feedbacks and incentive strategies must be assessed. This can further be used as levers to motivate employees to make use of data in the way company aims. While employees are provided new tools, but not shown the right approach to think in a different way about their jobs, the outcome won’t really change. Speaking about the process, several companies have unplanned barriers to make use of their data for decision making. Business processes might require being re-engineered to integrate big data analytics. This can be accomplished through documenting the steps in a process and where certain reports get leveraged for one decision. Recognition would go a really long way as you win on the basis of new use of data, it must be rejoiced and endorsed to create internal drive and motivate positive behaviors with data.

 

6.   Data Governance: Data governance eventually allows the company level sharing of data and the fuel that lubricates the functioning of a big data analytics practice. A data governance plan will make sure that computations used across the company are decided on the basis of input from across the company, the correct people have access to the right data and data lineage is set. Companies do not really look to a tool for solving data governance, as it is people work and it must happen. Data governance guides the way and often navigates through complex interactions. Building a data dictionary is a great place to begin with. A data dictionary is an important document wherein all available end-user evaluations and dimensions are officially described. In these interactions and misinterpretations regarding terms are recognized and corrected.

 

7.  Final Strategy: The final strategy is the combination of all the work an organization have done to this point and what makes all their previous work doable. Companies have identified all that requires to happen to make you reach your destination, but prior to beginning with any design, develop, training or re-creation of a business process, it is very important to prioritize the activities. For every suggestion that will assist bridge the gap from existing position to the future position, describe the feasibility and projected business value it will offer. The strategy must prioritize activities which are simplest to execute, but also offer fast success to the business. Other factors must include in the final strategy are staff availability or exterior assistance is needed, company’s budgeting process and a timeline which enables for celebration of incremental successes earned along the way.

So, encompass these elements of a successful big data analytics strategy to manage your company data effectively.

 

Understanding What Exactly Single Source of Truth Is and Why Must Data Marketers Care About It

 

For data marketers that have spent some time in the world of marketing analytics have heard about the single source of truth often think what it means exactly.  Unlike many slangs floating around in their industry, single source of truth is an extremely useful concept, which allows the data marketers to watch their performance very clearly and correctly than before.

What is Single Source of Truth?

 

Single source of truth (SSOT) is basically, a concept which originally derives from the information systems industry.  It is a single place where a company’s data is stored and if required, edited or updated. In this manner, everyone in the company works from the similar record, enabling for reliable, correct reporting, analysis and decision-making across the whole company. 

Having a single source of truth is important for bigger brands and companies which might have many teams and divisions, located around the world, all of them generating their own individual data.

For judging the efficiency of your whole marketing investment, data marketers must be able to see all your performance results together at one place for creating actionable marketing insights and fast reporting on campaign performance. 

Key Reasons Why Data Marketers Tries Really Hard to Create a Single Source of Truth:

 

• Different Data Sources: Nearly all marketing team depend on many different platforms, services and marketing channels. All of those tools have its unique approach to structure the data that it creates. Possibly it tracks a bizarre metric which nobody else does or it formats the dates with the help of pig Latin, which is a huge issue when you require combining the data from a source with data through all the other sources.

 

• Huge Volumes of Data: Data marketing tools produce huge volumes of data. For making the data useful, companies must make sure to collect the data must be gathered and analyzed on-time. Automated reporting tools assist you, but are still time-consuming chore to just integrate everything together, particularly if the data is not set up then, it becomes easy to integration.

 

• Inadequate Time and Expertise: Nearly all data marketing teams are busy with many other projects and because of that they do not get time or training to act as a part-time data herder. There are many software, but marketing single source of truth data is so personal and sophisticated, that just software cannot bring all your data together in a manner which is useful for data marketers.

Primary Approaches to Make a Single Source of Truth for Data Analytics:

 

• Figure Out Your Vital Questions: Data marketers think about the bottom-line impact. How exactly is marketing building value for the business when it comes to sales, leads and final conversions? And in what ways your different strategies, channels and campaigns delivering the desired outcomes successfully? You often oversee marketing for a credit union that wants to boost the volume of new members that are opening accounts. You might also want to know how much marketing requires spending for generating a lead.

 

• Identify Right KPIs for Every Question: What all KPIs will assist you get answers to those questions? Everyone believes that they know what they must be looking at. Usually, companies just monitor vanity metrics as those big numbers looks good, but don’t focus on whether your marketing is actually making an impact on the ultimate goals. Metrics like cost per conversion have to be more important for nearly all campaigns, as it will provide you the information you require for putting your outcomes in context and convey a bigger performance story. Cost per conversion is the KPI which will assist you realize how much every lead costs in terms of marketing expenditure.  

 

• Stock Your Data Sources And Reporting Requirements: The next step in handling the single source of truth data is taking a stock of all the data sources you have to monitor for generating those KPIs. It is very important to find out the frequency of how often you require delivering the reports. A lot of marketing agencies used to give reports every month, which is quite beneficial. You can receive a wider view of performance which evens out any routine swings. Make use of marketing dashboards which are filled with datasets that gets updated daily. This helps you find issues with campaigns early and redirect expenditure as required, as fast as possible. 

And that is a high-level approach to see what a single source of truth can do for company’s marketing. Through bringing all your sales, marketing and media data together in a unified manner, you can make it possible to generate reports for all the company channels and campaigns. As you are storing huge volumes of historical data in organized datasets, you can lay a strong foundation for complex data analysis that involves predictiveness and attribution. Making it a single source having multiple benefits!